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Who is Scott Borgenson? Profile from 2016 in “Institutional Investor”

(Note the connections)
CargoMetrics Cracks the Code on Shipping Data
Scott Borgerson and his team of quants at hedge fund firm CargoMetrics are using satellite intel on ships to identify mispriced securities.
By Fred R. Bleakley February 04, 2016
Link to article
One late afternoon last November, as a ping-pong game echoed through the floor at CargoMetrics Technologies’ Boston office, CEO Scott Borgerson was watching over the shoulder of Arturo Ramos, who’s responsible for developing investment strategies with astrophysicist Ronnie Hoogerwerf. At Ramos’s feet sat Helios, his brindle pit-bull-and-­greyhound mix. All three men were staring at a computer screen, tracking satellite signals from oil tankers sailing through the Strait of Malacca, the choke point between the Indian Ocean and the South China Sea where 40 percent of the world’s cargo trade moves by ship.
CargoMetrics, a start-up investment firm, is not your typical money manager or hedge fund. It was originally set up to supply information on cargo shipping to commodities traders, among others. Now it links satellite signals, historical shipping data and proprietary analytics for its own trading in commodities, currencies and equity index futures. There was an air of excitement in the office that day because the signals were continuing to show a slowdown in shipping that had earlier triggered the firm’s automated trading system to short West Texas Intermediate (WTI) oil futures. Two days later the U.S. Department of Energy’s official report came out, confirming the firm’s hunch, and the oil futures market reacted accordingly.
“We nailed it for our biggest return of the year,” says Borgerson, who had reason to breathe more easily. His backers were watching closely. They include Blackstone Alternative Asset Management (BAAM), the world’s largest hedge fund allocator, and seven wealthy tech and business leaders. Among them: former Lotus Development Corp. CEO Jim Manzi, who also had a long career at IBM Corp.
Compelling these investors and Borgerson to pursue the shipping slice of the economy is the simple fact that in this era of globalization 50,000 ships carry 90 percent of the $18.5 trillion in annual world trade.
That’s no secret, of course, but Borgerson and CargoMetrics’ backers maintain that the firm is well ahead of any other investment manager in harnessing such information for a potential big advantage. It’s why Borgerson has kept the firm in stealth mode for years. In its earlier iteration, from 2011 to 2014, CargoMetrics was hidden in a back alley, above a restaurant. Now that he’s running an investment firm, Borgerson declines to name his investors unless, like Manzi and BAAM, they are willing to be identified.
“My vision is to map historically and in real time what’s really going on in economic supply and demand across the planet,” says the U.S. Coast Guard veteran, who prides himself and the CargoMetrics team on not being prototypical Wall Streeters. “The problem is enormous, but the potential reward is huge.”
According to Borgerson, CargoMetrics is building a “learning machine” that will be able to automatically profit from spotting any publicly traded security that is mispriced, using what he refers to as systematic fundamental macro strategies. He calls the firm a new breed of quantitative investment manager. In unguarded moments he sees himself as the Steve Jobs or Elon Musk of portfolio management.
Though his ambitions may sound audacious, one thing is certain: Borgerson doesn’t lack in self-confidence. Over the past six years, he has secretly and painstakingly built a firm heavy in Ph.D.s that can manage a database of hundreds of billions of historical shipping records, conduct trillions of calculations on hundreds of computer servers and systematically execute trades in 28 different commodities and currencies.
For his part, Borgerson seems an unlikely architect of such a serious, ambitious endeavor. Easygoing and fond of joking with his colleagues, he is a hands-off manager who credits CargoMetrics’ investment prowess to his team. His brand of humor comes through even when he’s detailing the series of challenges he had starting the firm. After using the phrase “It was hard” several times, he pauses and adds, “Did I mention it was hard?” Although Borgerson declines to provide any specifics about Cargo­Metrics’ portfolio, citing the advice of his lawyers, performance during the three years of live trading apparently has been strong enough to keep his backers confident and his team of physicists, software engineers and mathematicians in place. “Hopefully, it won’t be too long before we can make a more significant investment,” says BAAM CEO J. Tomilson Hill. Former Lotus CEO Manzi is optimistic about the firm’s prospects: “It has an unbelievable edge with its historical data.”
CargoMetrics was one of the first maritime data analytics companies to seize the potential of the global Automatic Identification System. Ships transmit AIS signals via very high frequency (VHF) radio to receiver devices on other ships or land. Since 2004, large vessels with gross tonnage of 300 or more are required to flash AIS positioning signals every few seconds to avoid collisions. That allows Cargo­Metrics to pay satellite companies for access to the signals gleaned from 500 miles above the water. The firm uses historical data to identify cargo and aggregation of cargo flow, and then applies sophisticated analysis of financial market correlations to identify buying and selling opportunities.
“We’re big-data junkies who could not have founded CargoMetrics without the radical breakthroughs of this golden age of technology,” Borgerson says. The revolution in cloud computing has been instrumental. CargoMetrics leverages the Amazon Web Services platform to run its analytics and algorithms on hundreds of computer servers at a fraction of the cost of owning and maintaining the hardware itself.
At his firm’s headquarters — where the lobby displays a series of colored semaphore signal flags that spell out the mathematical equation for the surface area of the earth —Borgerson leads the way to his server room. It’s the size of a closet; inside, a thick pipe carries all the data traffic and analytic formulas CargoMetrics needs. That computing power alone would have cost $30 million to $40 million, Manzi says.
CargoMetrics is pursuing a modern version of an age-old quest. Think of the Rothschild family’s use in the 19th century of carrier pigeons and couriers on horseback to bring news from the Napoleonic Wars to their traders in London, or, in the 1980s, oil trader Marc Rich’s use of satellite phones and binoculars for relaying oil tanker flow.
Other quant-focused Wall Street firms are latching onto the satellite ship-tracking data. But, Borgerson says, “I would bet my life on a stack of Bibles that no one in the world has the shipping database and analytics we have.” The reason he’s so convinced is that from late 2008 he was an early client of the satellite companies that had begun collecting data received from space and on land to build a large database of all the world’s vessel movements in one place.
That’s what caught Hill’s eye at Blackstone when he learned of Cargo­Metrics a few years ago. BAAM now has a managed account with the firm. “If anyone else tries to replicate what CargoMetrics has, they will be years behind [Borgerson] on data analytics,” Hill says. “We know that a number of hedge fund data scientists want his data.”
But too much reliance on big data can go wrong, say many academicians. “There is a huge amount of hype around big data,” observes Willy Shih, a professor of management practice at Harvard Business School. “Many people are saying, ‘Let the data speak; we don’t need theory or modeling.’ I argue that even with using new, massively parallel computing systems for modeling and simulation, some forces in nature and the economy are still too big and complex for computers to handle.”
Shih’s skepticism doesn’t go as far as to say the data challenge on global trade is too big a puzzle to solve. When informed of the Cargo­Metrics approach, he called it “very valid and creative. They just have to be careful not to throw away efforts to understand causality.”
Another big-data scholar, Massachusetts Institute of Technology professor of electrical engineering and computer science Samuel Madden, also urges caution. “What worries me is that models become trusted but then fail,” he explains. “You have to validate and revalidate.”
Borgerson grew up in Southeast Missouri, in a home on Rural Route 5 between Festus and Hematite. His father was a former Marine infantry officer and police official, and his mother a high school French and Spanish teacher. The family traveled 15 miles to Crystal City to attend Grace Presbyterian Church, which was central to young Borgerson’s upbringing: There he was a youth elder, became an Eagle Scout and received a God and Country Award. The church was across the street from the former home of NBA all-star and U.S. senator Bill Bradley, whose backboard Borgerson used for basketball practice.
When it came to choosing what to do after high school, Borgerson was torn between becoming a Presbyterian minister and accepting an appointment to the U.S. Coast Guard Academy or West Point. He went with the Coast Guard because, he says, “the humanitarian mission really appealed to me, and I had never been on a boat before.”
At the academy, in New London, Connecticut, Borgerson played NCAA tennis and was also a cutup, racking up demerits for such antics as placing a sailboat on the commandant of cadets’ front lawn and leading bar patrons in a rendition of “Semper Paratus,” the school’s theme song. Still, he graduated with honors and spent the next four years piloting a 367-foot cutter — which seized five tons of cocaine in the Caribbean — then captaining a patrol boat that saved 30 lives on search-and-rescue missions. From 2001 to 2003 the Coast Guard sent Borgerson to the Fletcher School at Tufts University to earn his master’s of arts in law and diplomacy. While at Tufts he volunteered at a Boston homeless shelter for military veterans and founded a Pet Pals therapy program for senior citizens.
Following graduation, from 2003 to 2006, Borgerson taught U.S. history, foreign policy, political geography and maritime studies at the Coast Guard Academy, and co-founded its Institute for Leadership. While there he would get up at 4:00 each morning to work on his Ph.D. thesis exploring U.S. port cities’ approaches to foreign policy. He would also travel to Boston to complete his course work at Tufts and meet with his adviser, John Curtis Perry.
Borgerson’s military allegiance runs deep. One weekend last fall he played football in a service academy alumni game. On another he attended the Army-Navy game. Still militarily fit at age 40, the 6-foot-5 Borgerson works out regularly at an inner-city gym aimed at helping youths find an alternative to gang violence; a few weeks ago he was there boxing with ex-convicts and lifting weights.
Leaving the Coast Guard was a hard decision for Borgerson, resulting in part from his frustration with the military bureaucracy’s stymieing of his bid to get back to sea for security missions. With his degrees in hand, he applied for a fellowship at the Council on Foreign Relations. During the application process he met Edward Morse, now global head of commodities research at Citigroup. Morse was on the CFR selection committee in 2007 and recommended Borgerson as a fellow.
Morse introduced Borgerson to commodities, and to trading terms like “contango” and “backwardation.” Morse himself had, earlier in career, gotten the jump on official oil supply data by hiring planes to take photos of the lid heights of oil tanks in Oklahoma’s Cushing field.
Working for the CFR in New York reconnected Borgerson with his Missouri roots. Bill Bradley’s aunt called the former senator to say: “The son of a family who went to our church in Crystal City is in New York. Would you welcome him?” Bradley did — and would later play a part in Borgerson’s career development.
While at the CFR, Borgerson became an expert on the melting of the North Pole ice cap, writing numerous published articles on its implications; this led him to co-found, with the president of Iceland, the Arctic Circle, a nonprofit designed to encourage discussion of the future of that region. Borgerson recently spoke to 50 international generals and admirals about the Arctic and is co-drafting a proposal for a treaty between the U.S. and Canada that would help resolve the differences the two countries have in allowing international ship and aircraft travel through the Northwest Passage.
His Arctic research led to an aha moment early in 2008, while he was still with the CFR, on a visit to Singapore and the Strait of Malacca with his Fletcher School classmate Rockford Weitz and their former Ph.D. adviser, Perry. Seeing the mass of ships sailing through the strait, Borgerson and Weitz decided to build a data analytics firm using satellite tracking of ships.
Like many successful entrepreneurs, the two struggled to find financing before reaching out to a network of friends and their contacts. One was Randy Beardsworth, who had sat with Borgerson at a 2007 Coast Guard Academy dinner, where Beards­worth, then the Coast Guard’s chief of law enforcement in Miami, was the guest speaker. Borgerson “made references to history and literature, and I thought, ‘Here is a sharp guy,’” recalls Beards­worth. “We have been friends ever since.”
But Borgerson didn’t turn to his new friend in his initial fund-raising. “He came to me in 2009, after he had been turned down by 17 VCs, was maxed out on his credit card, was married and had a newborn son,” says Beardsworth, who was reviewing the Department of Homeland Security as part of the Obama administration’s transition team. Beardsworth came to the rescue, not only committing to invest a small amount but introducing his friend to Doug Doan. A West Point graduate and Washington-­based angel investor, Doan took to Borgerson right away. “To be honest, it wasn’t his idea, it was Scott I invested in,” says Doan, who provided $100,000 in capital and introduced Borgerson to a few friends, who added $75,000. Manzi came on board as an investor in 2009, having been asked by Bradley to check out Borgerson’s plan for a data metrics firm. (Manzi knew Bradley from the late 1990s, when the latter was considering a run for U.S. president.)
With Doan, Doan’s friends and Manzi as investors, CargoMetrics was finally able to garner its first venture capital commitment in early 2010, from Boston-based Ascent Venture Partners. That gave the start-up the capital it needed to hire a bevy of data scientists to build an analytics platform that it could sell to commodity-trading houses and other commercial users. In 2011, CargoMetrics added Summerhill Venture Partners, a Toronto-based firm with a Boston office, to its investor roster, raising roughly $18 million from venture capital and angels for its data business.
By then Borgerson had already begun to contemplate converting CargoMetrics from an information provider into a money manager; he saw the potential to extract powerful trade signals from its technology rather than share it with other market participants for a fee. Among those he consulted was serial entrepreneur Peter Platzer, a friend of one of CargoMetrics’ original investors. Platzer, a physicist by training, had spent eight years as a quantitative hedge fund manager at Rohatyn Group and Deutsche Bank before co-founding Spire Global, a San Francisco–­based company that uses its own fleet of low-orbit satellites to track shipping, in 2012. “We had lengthy conversations on how to set up quant trading systems and how [commodities giant] Cargill had made a similar decision to set up its own in-house hedge fund to trade on the information it was gathering,” recalls Platzer. So Borgerson reset his course. Doan describes the decision as a “transformative moment” for the CargoMetrics co-founder. “The military trains you to be a strategic thinker,” Doan explains. “Scott had been tactical until then, making small pivots, and like a general who sees the theater of war, he moved into strategic mode.”
Borgerson’s ambition to succeed was in no small part fueled by the early turndowns by many venture capital firms and a fierce determination to best the Wall Street bunch at their own game. “There’s a lot that motivates me, including — if I’m honest — I have a big chip on my shoulder to beat the prep school, Ivy League, MBA crowd,” he says. “They’re bred to make money, but they’re not smarter than everyone else; they just have more patina and connections.” (Bred differently, he spent last Thanksgiving visiting his parents in rural Missouri. After breakfast he and his father were in the woods, shooting assault guns at posters of terrorists, with Gunny, his father’s Anatolian shepherd dog.)
Borgerson’s plan was not met with enthusiasm from the company’s then co-CEO, Weitz. CargoMetrics had been gaining clients and meeting its goals, and was on its way to becoming a successful data service provider. Weitz, who now is president of the Gloucester, Massachusetts–based Institute for Global Maritime Studies and an entrepreneur coach at Tufts’ Fletcher School, did not return e-mails or phone calls asking for comment. For his part, Borgerson says: “A ship cannot have two captains. The company simply matured and evolved into a streamlined management structure with one CEO instead of two.”
Eventually, Doan went along with Borgerson’s plan. “We believe in Scott and that shipping holds the no-shit, honest truth of what the economy is doing,” he says. But buying out the venture capital firms several years ahead of the usual exit time would require a hefty premium over what they had invested.
Once again Borgerson’s early supporters played a key role. Manzi, a fellow Fletcher School grad who had mentored Borgerson since the company’s early days, put up more money (making CargoMetrics one of his single largest investments) and introduced him to a powerful group of wealthy investors. Separately, the CFR’s Morse suggested that Borgerson meet with Daniel Freifeld, founder of Washington-based Callaway Capital Management and a former senior adviser on Eurasian energy at the U.S. Department of State. Impressed by Borgerson’s “intellectual honesty, vigor and more than four years of historical data,” Freifeld brought the idea to a billionaire third-party investor, who took his advice and became one of CargoMetrics’ largest backers. “I would not have suggested the investment if CargoMetrics had not done the hard part first,” adds Freifeld, declining to name the investor.
A chance encounter in the fall of 2012 gave the CargoMetrics team its first taste of real Wall Street trading. Attending an Arctic Imperative conference in Alaska, Borgerson met the CIO of a large investment firm, whom he declines to name. When Borgerson confided his ambition and that CargoMetrics had developed algorithms to trade on its shipping data once it was legally structured to do so, the CIO suggested CargoMetrics provide the analytical models for a separate portfolio the money manager would trade. Live trading using CargoMetrics’ models began in December 2012. Manzi brought in longtime banker Gerald Rosenfeld in 2013 to craft and negotiate the move to make CargoMetrics a limited liability investment firm. Rosenfeld acted in a personal role rather than in his position as vice chairman of Lazard and full-time professor and trustee of the New York University School of Law. The whole process took a year and a half. During that time Blackstone checked in as an investor.
Bradley, now an investment banker, has yet to invest in CargoMetrics, explaining that he is unfamiliar with quantitative investing. But he may eventually invest in Borgerson’s firm, he says, because “we are homeboys. I believe in him and that things are going to work out ” — pausing to add with a smile, “based on my vast quant experience, of course.”
Borgerson has been in stealth mode since CargoMetrics’ early days, when he moved the firm from an innovation lab near MIT because the shared space was too open. He is much more forthcoming when boasting of the firm’s “world-class talent.” The team includes astrophysicists, mathematicians, former hedge fund quants, electrical engineers, a trade lawyer and software developers. Hoogerwerf, who has a Ph.D. in astrophysics from the Netherlands’ Leiden University, built distributed technical environments for scientists and engineers at Microsoft Corp. Solomon Todesse, who works on quant investment strategies, was head of asset allocation at State Street Global Advisors. Aquil Abdullah, a team leader in the engineering group, was a software engineer in the high-performance-computing group at Microsoft. And senior investment strategist Charles Freifeld (Daniel’s father) has 40 years’ experience in futures and commodities markets, including nine with Boston-based commodity trading adviser firm AlphaMetrics Capital Management.
“All were self-made people; none were born with a silver spoon,” Borgerson notes. One of his blue-collar-­background hires was James (Jess) Scully, who joined as chief operating officer in 2011, after his employer Interactive Supercomputing was acquired by Microsoft.
“The team we built treasures team success, which is Scott’s motto,” Scully says. “We want shared resources, one P&L, not ‘How much money did my unit make?’” Both Scully and Borgerson say Cargo­Metrics is like the Golden State Warriors, a leading NBA basketball team known for putting aside personal glory and playing as a band of brothers having fun.
Borgerson says he fosters a no-ego policy with “lots of play because investment teams are built on trust, and playing together builds trust.” Team building at CargoMetrics includes pub crawls, picnics at Borgerson’s house, poker nights, volunteer work in a soup kitchen for the homeless, Red Sox games and visits to museums.
Trips to the Boston docks or Coast Guard base are intended to remind the CargoMetrics team of the real economy. There are also occasional “touch a tanker” days. On one visit to a tanker, everyone was amazed that the ship was the size of a city building, Borgerson says. “They could smell the salt on the deck,” he recalls. “Wall Street can lose sight of the real fundamentals in the world. I don’t want that to happen here.”
Unlike the Rothschilds 200 years ago, only a small percentage of the trades that CargoMetrics makes relate to beating official government data. Most simply are aimed at identifying mispricings in the market, using the firm’s real-time shipping data and proprietary algorithms.
At a whiteboard in his conference room, Borgerson sketches out CargoMetrics’ general formula. He draws a “maritime matrix” of three dynamic data sets: geography (Malacca, Brazil, Australia, China, Europe and the U.S.), metrics (ship counts, cargo mass and volume, ship speed and port congestion) and tradable factors (Brent crude versus WTI, as well as mining equities, commodity macro and Asian economic activity). Using satellite data with hundreds of millions of ship positions, CargoMetrics makes trillions of calculations to determine individual cargoes onboard the ships and then to aggregate the cargo flows and compare them with historical shipping data. All that leads to the final comparisons with historical financial market data to find mispricings. If CargoMetrics observes an appreciable decline in export shipping activity in South Africa, for example, its trading models will determine whether that is a significant early-warning sign by considering that information alongside other factors, such as interest rates. If Cargo­Metrics believes a decline in the rand is forthcoming, it might short it against a basket of other currencies. “This is like a heat map showing opportunity,” Borgerson says, noting that CargoMetrics is not trading physical commodities. “We are agnostic on whether to be long or short, and let the computers spot where there is a mispricing and liquidity in the markets.” He sums up his simple, but still less than revealing, process by writing on the whiteboard “Collect, Compute, Trade.”
Borgerson says CargoMetrics is building a systematic approach that will work even when cargo cannot be identified — on containerships, for instance. It already knows a large percentage of the daily imports and exports into and out of China and island economies such as Japan and Australia. And although the firm cannot glean from its calculations on satellite AIS data the type of cargo, such as iPhones from China, it can measure total flow, which shows present economic activity. Cargo­Metrics’ data scientists are working on linking such activity to the firm’s data set of the past seven years to measure the evolving global economy. That will lead, Borgerson maintains, to more trades on currencies and equity index futures and, eventually, trades on individual equities. “Uncorrelated” is a mantra of Borgerson and his team. Well aware that correlated assets sent the performance of most asset managers, including hedge funds, plunging in the financial crisis, CargoMetrics is determined to come up with an antidote. Careful not to say too much, Borgerson lays out the simple principle that the process starts with placing many bets among uncorrelated strategies in different asset classes, like commodities, currencies and equities.
The goal is diversification, staying as market neutral as possible and remaining sensitive to tail risk in different scenarios. CargoMetrics’ analytic models help find asset classes that are outliers. Those may include a publicly traded instrument such as oil, another commodity or an equity for which shipping information was a leading indicator during times when other asset classes marched in lockstep. The historical ship data is then blended with this new information to seek opportunities. Identifying mispriced spreads among different trades within an asset class is another way of avoiding the calamity of correlation. Borgerson says the firm’s models will find instances where one type of oil should be a short trade and another a long one. The same goes for whole asset classes — shorting one that will benefit if virtually all asset prices plunge and buying another that will rise when oil prices gain. “We’re counting cards with the goal of being right maybe 3 percent more than we are wrong, as a way of making profits during good times and staying afloat during times of sudden, unpredictable but far-reaching events,” Borgerson says. The key, he adds, “is to know your edge and spread your risk.” CargoMetrics’ uncorrelated approach worked during the dismal first three weeks of this year, says Borgerson. Dialing down risk as volatility in the markets soared, the firm was on track in January to have its best month since it began trading.
To improve the firm’s models, eight of its data scientists hold a weekly strategy meeting, nicknamed “the Shackleton Group” after the band of sailors shipwrecked in the Antarctic from 1914 to 1917. Hoogerwerf and Ramos co-lead the group. At one recent meeting they were deciding how much risk, including how much liquidity, there was in a possible strategy; reviewing whether to keep previous strategies; and assigning who would research new ones.
The Shackleton Group’s meetings are free-form, with a lot of “I’ve got an idea” interjections that disregard official roles. “We hit the restart button a lot,” says Ramos, a former director of business intelligence and a quantitative economist at law firm Dewey & LeBoeuf who joined CargoMetrics in late 2010. “That’s why our motto is ‘Never lose hope.’” A bet on oil, related to Russia’s production, was stopped at the last minute in 2014, when Russia invaded Ukraine. Some currency-trading strategies have been abandoned in theory or after failing. Strategies the Shackleton Group likes are passed on to the firm’s investment committee of Borgerson, Scully and Ramos for a final decision. CargoMetrics has a unique set of big-data challenges. Historical shipping patterns may not be as useful in the new global economy now that shipping freight prices are plunging, a sign that trade growth rates may be changing. And analysts point out how hard identifying oil cargo can be in certain locations and instances, even in more-­predictable economic times. “While it may be easy to say that ships leaving the Middle East Gulf are typically carrying crude oil, knowing the type of crude is sometimes quite difficult,” says Paulo Nery, senior director of Europe, Middle East and Asia oil for Genscape, a Louisville, Kentucky–based company that analyzes satellite tracking of ships. Borgerson maintains his team is well aware of the dangers of data mining and getting swamped by noise. “If you run computers hard enough, you can convince yourself of anything,” he says. To make sure CargoMetrics’ algorithms for identifying cargo are valid, the firm spot-checks manifest data filed at ports and imposes statistical confidence checks to guard against spurious correlations.
Getting the jump on official government statistics is likely to become tougher too thanks to the recently formed High-Level Group for the Modernization of Official Statistics. Although the U.S. is not a member, Canada is a key player helping to lead the mostly European nation group (including South Korea) in coming up with a global blueprint for measuring and reporting economic activity.
Reflecting on his journey to Wall Street — raising money, hiring employees with different skill sets, making changes to Cargo­Metrics’ culture, overcoming legal and regulatory hurdles — almost gives Borgerson second thoughts about whether he would do it again. “I’ve sailed ships through tropical storms, captured cocaine smugglers and testified before Congress [on his Arctic research],” he says, “but this was the hardest.”
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[Guide] 1000 Ancient Tunnels runs + Comprehensive Guide to High Rune Drop Rates - part 2

Introduction

Hello all, Initially this post was meant to be a highlight of 1000 Ancient Tunnel runs, which I have completed over the last few months (had a break in between). I wanted to sum it all up somehow and since I am absolutely fascinated by the entire rune concept and their drops, I decided to go a bit deeper and...create a guide with some helpful tips and in-depth analysis.

Purpose

The purpose of this guide is to show the best non-LK way to farm high runes and to highlight how many runs you need to perform before you will see some results, which is what I believe setting this guide apart from other articles I have seen so far. Also, there are at least 3 main sources for rune drop odds, each showing different numbers, so I wanted to confirm that by myself which one is good. Which means digging into text files.
I'm saying non-LK, because nothing beats Lower Kurast chest farming, mathematically it is the fastest but at the same time - the most boring way. You will turn yourself into a human bot, there won't be any experience, any items (besides charms and jewels). I did my fair share (1200 runs) and that's it.
Big shoutout to preppypoof for creating the original guide! I can confirm that vast majority of the information that can be found there is correct.

Who am I?

I am a data analyst, ex semi-pro poker player and a fan of Diablo II of course :) I like numbers and statistics - that is exactly why runes are so fascinating for me.

Definition of a High Rune

Many say it starts from Vex, I would say it starts from Gul, since it is the first rune you can't get from Countess (from her special drop). On the other hand, Ist is rarer than Gul, but then again - it doesn't make sense to transmute two Ists into Gul, especially since Ists are quite valuable for MFing.

A quick recap

For those who don't understand how this rune drop system works, there are essentially 3 main steps that have to be fulfiled before you will see your rune. If you are not interested, feel free to skip to the next paragraph.
>!1. Select the "Good" category Depending on act, difficulty level and monster type (melee, range, wraith, cow, etc.), each monster has its own category, based on which game will decide what items it can choose from upon its death. Most popular will be most likely Act 5 (H) H2H C, which is common for melee monsters in Level 85 areas. There are the following possibilities: - NoDrop 100 - gld 21 - Act 5 (H) Equip C 16 - Act 1 (H) Junk 21

- Act 5 (H) Good 2

sum or probabilities: 160!<
We won't go into any more details, all you need to know is that: a) you want to hit Act 5 (H) Good (2/160 or 1.25%) b) ideally, you want to minimize NoDrop, so that your odds for hitting Act 5 (H) Good will increase (we will get to that later)
2. Selecting Runes 17 from Act 5 (H) Good Welcome inside the Good category.
>!- Jewelry C 60 - Chipped Gem 4 - Flawed Gem 10 - Normal Gem 14 - Flawless Gem 28

- Runes 17 14

sum or probabilities: 130!<
You see that Runes 17? This is what we aim at (14/130 or 10.8%). If you would like to know more details about what Runes 17 is, please visit preppypoof's guide.
3. Selecting rune quality Welcome inside the Runes 17 category. Now it's time to select your prize. Runes are organized in categories, two in each in all categories all the way up to Runes 16 (besides Zod, which is alone in its top tier category called Runes 17), that's why you have 2*16 + 1 = 33 runes in total.
Random number generator will go in a "stairs-like" sequence: - let me try to get you that Zod, 1 in 5171. Oh, we missed? Let's go one step down to Runes 16 (that will happen in 5170 out of 5171 cases) - Welcome in the Runes 16 category. We have 2 offerings, especially for you my friend: Cham and Jah. Every single time the top rune (here it's Cham) will have a probability of 2 and the bottom rune will have a probability of 3 (here it's Jah). The difference will be the last part, which will determine the chance to "step down". At the highest levels, this chance will be huge, but it will gradually go down.
In this example, it's 2941. Add those 2 and 3 and you have 2946 of that probabilites. So, after going down to Runes 16, now your options are: - 2/2946 to get a Cham Rune - 3/2946 to get a Jah Rune - 2941/2946 to step down to Runes 15
The sequencer will go all the way down to Runes 1, where you have El and Eld - unless it will hit something during the process (which is what we want).
>! So in short, we must hit all 3 things at once: - Good category - Runes 17 - our desired rune Multiply all those probabilities and you will get some astronomical numbers, but don't panic yet :)!<

Rune Odds Tables

This one will be almost the same as in the original guide. However, I have found a small error in preppy's calcs. I got the same numbers from Zod till Ohm, but starting from Vex and below your chances of hitting those runes are slightly smaller than in the quoted article. I found the reason: preppy took the remaining probability (if you are looking at the example above - that would be that 2941) as the total sum of probabilities, whereas the total is bigger by 5 (that would be 2946 in our example). This error continues all the way down and most likely throughout the rest of the columns (for Wraiths, Cows, Champions, etc.). I will only provide you with an updated table for Regular monsters and in a moment you will see why.
Rune odds for /players 1
Rune (Others / Regular) Chance Chance of ... or better
Zod 3 841 314 3 841 314
Cham 1 471 885 1 064 137
Jah 981 256 510 509
Ber 1 095 823 348 264
Sur 730 549 235 837
Lo 810 410 182 677
Ohm 540 273 136 518
Vex 569 154 110 107
Gul 379 436 85 342
Ist 401 293 70 376
Mal 267 529 55 719
Um 272 924 46 272
Pul 181 950 36 891
Lem 138 358 29 348
Fal 92 238 22 606
Ko 71 100 17 458
Lum 47 400 12 909
Io or Worse 774 758
While it is interesting to note that Ber Rune is rarer than Jah, getting a Cham Rune isn't that much far away, it is just 1.5x rarer than Jah. That "1.5x rarer only" means additional 2100 AT runs though…
It is absolutely shocking how much rarer Zod Rune is from Cham Rune (2.6x rarer). It reminds me of poker hands: AA is just miles and miles away from the 2nd most powerful hand - KK. Then the differences between the next hands get smaller, similar thing can be observed here.

Players X setting vs Rune drops

Remember first point of the sequence? Here is where the players X setting come into play. By increasing the number of players you can decrease the NoDrop value. It goes as per so called NoDropExponent, there is complicated formula behind it, but let's get down to business.
In short: every odd number will increase the player bonus, that's why you want to select players 1/3/5/7, but never 2/4/6/8 (unless you want more experience and you are a super fast killer anyways).
I think this is another mistake that I found in preppy's article. He said that increasing players setting from 1 to 3 will yield you around 30% more runes. Well...it looks like the increase is much bigger than that! Here is a breakdown for numbers up to NDE=4 (which is same as players 7/8 and is max what can be reached in a single player game where there are no party members around you. Higher NDEs are possible on multiplayer).
​
NoDropExponent 1 (players 1) 2 (players 3) 3 (players 5) 4 (players 7)
New NoDrop 100 38.46 19.38 10.8
Prob "Good" 1.25% 2.03% 2.52% 2.82%
Hitting Runes 17 0.135% 0.219% 0.271% 0.304%
% increase - 62.48% 24.03% 12.11%
It means that you should farm runes on at least /players 3 setting, because I am almost sure you won't take more than 62.48% time to kill them. After that point is where the fun begins. Going from p3 to p5 is still doable, but from p5 to p7 is a tricky one.
Interpretation: if your current clear speed on p5 is 5 mins, then you should clear p7 in a time no longer than 5.6 mins (5 mins 36 seconds). In other words: increasing players difficulty setting, which will increase monsters hit points from 300% to 400% (+33.3%) cannot take you more than additional 36 seconds to clear, otherwise you're better off on p5.
Tip: players X settings does have an effect on popables (chests, urns, etc.). It is possible to first clear the area on a lower settings and then after that changing it to p7/8 and then popping the chests/urns. Whether you consider that strategy as cheesy or not I will leave that up to you. Personally I find it troublesome to constantly switch between the settings.

Expected Value - introduction

This is my favorite part and what I think sets this guide apart from the others - the expected value. Well almost, there is something similar in an absolutely great guide about LK vs Travincal vs Cows, where one guy has even used some serious high-level math (calculus etc.), but the results are still close enough so that we can use our basic approach.
So, what is that expected value? Basically I will try to answer a very frequent question: how many runs you need to complete before you will get that Jah Rune. I will give you the exact number, with one small "but": you need to understand that because of the RNG (Random Number Generator) nothing is certain for 100%. It's the same as with rolling a dice. If you want to roll a "6", you have 1 in 6 chances to hit it. Your expected value after 6 rolls is 1 (1/6 / 6 = 1), meaning that after 6 rolls you expect to hit that 6 once. However, it is totally possible that you will hit that 6 in a first roll or that you won't hit it in your 12th roll. Same with runes. Below are your chances (or if you will: confidentiality levels) per each EV (these values are similar to almost every drop in Diablo II):
# of EVs Confidentiality Level
1 63.212%
2 86.466%
3 95.021%
4 98.168%
5 99.326%
6 99.752%
7 99.909%
8 99.966%
9 99.988%
10 99.995%
The way I calculated the EV includes normalizing everything to a regular monster (for which we already know the rune drop odds). We need to do this first before coming back to the EV.

Normalization

I will Ancient Tunnels as an example. What you need to do is to calculate how many "regular kills" you can get per one full clear (full clears are better if you are looking for runes). That means, you need to translate every champion/unique, every urn, every chest, every boss and what not - into a regular monster. How to do that?
For champs and bosses you can use drop calculators, even though they show incorrect values (way too high, in reality your chances are better), but the proportions are maintained. I will use preppy's tables since I have confirmed them so I know they are good to use. Small note: this is a third and last mistake that I found in preppy's guide. He claimed that players settings increase will have a very small effect on rune drop odds for champions and bosses. The answer is: it doesn't have any effect (just like your Magic Find %), since there is no NoDrop value, so there is nothing to be decreased. I will use a Zod Rune as an example, but you can use any rune that you want.
​
Regular p1 Regular p7 Champ Champ p1 proportions Champ p7 proportions Unique Unique p1 proportions Unique p7 proportions
3 841 314 1 700 319 1 600 548 2.4 1.07 744 255 5.16 2.29
As you can see, if you play on p1, then killing a champ or a boss makes a massive difference in terms of runes. Once you switch to p7, there is almost no difference between a regular and a champ. I measured the average number of bosses and champions over the course of ~~ 30 runs. Same for regulars and urns. Last thing that might be coming to your mind: how the heck can you know what are the drop odds for urns and chests?!
There is another great guide made by Urlik. It was for 1.10, when rune drop odds were less optimistic, so I can't rely on exact numbers, but...I can rely on proportions. In his guide, Urlik has found out the mean number of runes produced per kill at p8 (which is same as p7). Like I said, we cannot take these numbers directly, but we can copy/paste them into Excel and then get our proportions. Our baseline will be the first line: Melee/Cast/Missile. Let me present you the rest of the important proportions.
vs Regular
Special Chest 16.64
Special Chest - Locked 22.29
Sparkly Chest 37.60
Type IV (like Urns, Jars, Baskets) 1.16
Type III (like Rat Nests, Goo Piles, Jugs) 5.55
Type I (regular chests) - Locked 11.09
Type I & II 4.16
There is way more than that, but these are the objects that you will mostly encounter. As you can see, Sparkly chests and special chests (like those in LK or behind Mephisto or in River of Flame) = are your best friends. So essentially: popping one special chest is the same as killing 16.64 or 22.29 regular monsters, depending whether a chest is locked or now (no wonders people like LK chests, although their dropped is bugged). There is a table for that too. In AT, chance for a chest to be locked is 16.5%.

Total regular kills and kills per minute

We are almost there. Now we are at the most crucial point of this article - calculating total kills normalized to a regular. You can do the same for your own map (The Pit, Chaos, you name it). Here is how it looks like for my Ancient Tunnels at p7. What you are looking at are the average numbers of monsters/urns/whatever I have encountered over the course of 15-30 measured runs, it is time taking, you need to count it and then write it all down somewhere. Kind of self data collection. Remember: each map seed is different and my AT map won't be the same as yours. Map rolling is actually another interesting topic, AT holds few secrets which I will reveal later.
​
Nominal Normalized
Regular (x1.0) 86.7 86.7
Champ (x1.07) 3.5 3.73
Unique 5.2 11.92
Sparkly (x37.6) 1 37.6
Type IV (Urns) (x1.16) 38 44.25
Type III (x5.55) 3 16.64
Type I 1 5.3
TOTAL 206.13
Average time per full clear 3.6
P7 kills per minute 57.26
The way I derived that 5.3: = (0.165 * 11.09) + (1-0.165) * (4.16) = 5.3
Okay folks, the number required here is 206.13 and 57.26. Remember, that's on p7. You can do the same for p5 and compare your results with p7, but one important thing: first you will need to translate all p7 kills into p5 kills. One regular p7 kill is worth 1.12 regular p5. So in my example, that 86.7 would translate to 97.2 p5 kills. Don't forget that player bonus doesn't apply on champs/bosses, it's just the proportions will be different.
Kills per minute is in my opinion your main metric you should be monitoring in order to gauge your progress and make a decision whether to step up the players settings or not. Going from p5 to p7 will of course bring you some more runes, but it can decrease your normalized kills per minute. Make sure to maximize kills per minute by: - choosing the right players settings (As a rule of thumb, if you can already one shot everything on a current setting, then it's usually good idea to increase players X) - equip your max killing gear (MF doesn't matter, although it's still good to have some! I have 182)

Expected Value - # of runs required per rune

Time for the final results you've been waiting for :) Just some small remark: all objects "kills" in Act 2 can be calculated towards your final result up to Lo Rune, because Lo is max what these objects can drop in Act 2 (Act 1: Vex, Act 2: Lo, Act 3: Ber, Act 4: Cham, Act 5: Zod). In my case, they make for the ~~ 50% of the total "kills" which is both good and bad.
Good, because there is no there is almost no way you can kill 11 monsters faster than you can pop 10 urns (if you can, then most likely AT is not for you anyways, Cows will be faster).
Bad, because that means AT is not that great of a place to hunt for Sur+ runes, you will see what I mean when I will compare it against Chaos Sanctuary.
Assumptions: - p7 - One AT run # of regular kills up to Lo: 206.13 - One AT run # of regular kills up to Zod: 102.35 (substract all objects from the total result) - Number of AT runs: 1000
​
Rune EV Actually found # of AT runs to realize one EV
Zod 0.060 16 613
Cham 0.157 1 6 366
Jah 0.236 1 4 244
Ber 0.211 4 739
Sur 0.317 3 159
Lo 0.574 1 740
Ohm 0.862 1 1 160
Vex 0.818 1 222
Gul 1.227 1 815
Ist 1.160 1 862
Mal 1.741 4 574
Um 1.706 1 586
Pul 2.559 5 391
Lem 3.366 4 297
Fal 5.049 3 198
Ko 6.55 10-12+
Lum 9.82 10+
Io or Worse 601.83
And there you have it :) How to interpret these results? I think it went quite well. Clearly, Cham Rune destroyed everything in this run! Normally, there is a 63.2% chance to find it within one EV (6366 runs), but the lucky run was run #571 :) Jah Rune was found in Drifters Cavern, so technically it wasn't in AT, but I since I did that run kind of "in between" and for fun, I decided to include it anyways. That was before run #546.
Fact: I got plenty of runes from the urns/chests/jugs: Gul, Um, Mal, Pul, Lem and countless Ko, Io, etc.
Q: Ok, so you are saying that after 1000 runs I am guaranteed to get an Ist Rune (EV: 1.16), right? A: Not quite, you are guaranteed to fulfil your EV for an Ist Rune after 1000 AT runs (1.16 runes after 1000 runs or 1 rune after 862 runs to be precise), which gives you 63.2% confidence to get it. Wanna 86.5% confidence? Do 2000 runs. Wanna 95% confidence? Do 3000 runs. Wanna 99.995% confidence? Do 10000 runs.

Ancient Tunnels vs Countess

For runes up to Ist, Countess is your best source. I will use the results from 1000 Countess Runs done by dbrunski125, who has inspired me to my own Human Bot Project (love that name!). Worth noting is the fact that it is possible to do one Countess run in 30-40 seconds, so you will complete 1000 Countess runs ~~ 6x faster.
1000 Ancient Tunnels 1000 Countess Runs
Zod
Cham 1
Jah 1
Ber
Sur
Lo
Ohm 1
Vex
Gul 1 1
Ist 1 0
Mal 4 6
Um 1 8
Pul 5 10
Lem 4 9
That shouldn't be a surprise, after all - this guide is about high runes especially, but that is just out of curiousity ;) But then again, it's kind of robotic, just killing one single boss on p1, almost no items, no challenge.
In AT, I can constantly challenge myself, tweak with the gear, keep on improving my run times, get those elusive ethereal items, find mythical TC87 items and then also find some runes. Pure Diablo experience at its finest.
​

Ancient Tunnels vs Chaos Sanctuary

Assumptions: - players 7
- One CS run # of regular kills up to Zod: 408.91
- 1000 runs
Rune EV AT EV CS CS/AT # of AT runs to realize one EV # of CS runs to realize one EV
Zod 0.060 0.229 3.81 16 613 4 361
Cham 0.157 0.628 4.00 6 366 1 593
Jah 0.236 0.941 4.00 4 244 1 062
Ber 0.211 0.843 4.00 4 739 1 186
Sur 0.317 1.264 4.00 3 159 791
Lo 0.574 1.14 1.98 1 740 877
Ohm 0.862 1.71 1.98 1 160 585
Vex 0.818 1.623 1.98 1 222 616
Gul 1.227 2.435 1.98 815 411
Ist 1.160 2.302 1.98 862 434
Mal 1.741 3.453 1.98 574 290
Um 1.706 3.385 1.98 586 295
Pul 2.559 5.077 1.98 391 197
Lem 3.366 6.677 1.98 297 150
Fal 5.049 10.015 1.98 198 100
​
As you can see, on average CS will provide you 2x more runes up to Lo (or same number but 2x faster), but I am not really sure you can run it in a time no longer than 2x AT time. You can try to compare it against p5 CS, which will be definitely faster and will yield only ~~ 12% less runes. Very interesting :)
However, from Sur onwards CS is clearly better. CS collects additional points for a high monster density (roughly 200, out of which around 60-65 are wraiths, which have 3.5x the chance compared to a regular).
I am currently 59 runs into CS. For now, I will stick to AT, until I will find ethereal Colossus Blade for runeword Death (the EV for finding that thing is ~~ 1477 runs). Once done, I will switch to CS, since I need Lo, but specifically Sur for runeword Pride for my merc (it required Cham as well, which I already have).
​

Farming Lo Runes - time efficiency

I will take Lo as an example to illustrate. I will assume that you want that Lo Rune at all costs to the point that you sacrifice each Ohm, Vex, Gul. I will calculate each Ohm as 1/2 of a Lo, Vex as 1/4 of a Lo and Gul as 1/8 of a Lo - this isn't a fully correct approach, but I don't feel like doing calculus :)
My EV for Lo (farmed directly or cubed up) is: = 1 * 0.574 + 0.5 * 0.86 + 0.25 * 0.82 + 0.125 * 1.23 = 1.363 or after 733 runs
Currently, my average run takes 3.6 mins (massive improvement compared to like 7-8 minutes on p3/p5 back in the days). This translates into 44h or AT running. How does that compare vs LK / Travincal / Cows? I'm going to quote numbers from this great article:
Area: LK p7/8 Average runs to cube/farm Lo: ~1433 runs Average run-time and time needed to farm Lo: 25s: ~9.9h 21s: ~8.4h 18s: ~7.2h
Area: Travincal p3 Character: sorceress - for barbarian 55% hork divide numbers of runs by 1.597, and for 56% hork divide by 1.608 Average runs to cube/farm Lo: ~1725 runs Average run-time and time needed to farm Lo: 26s: ~12.5h 22s: ~10.5h 18s: ~8.8h
Area: Cows p5 Cows killed per run estimate: ~400 Average runs to cube/farm Lo: ~281 runs Average run-time and time needed to farm Lo: 4m 30s: ~21.1h 3m 30s: ~16.4h 2m 30s: ~11.7h
And now AT p7: Average runs to cube/farm Lo: ~733 runs
3m 36s: ~ 44.0h
If you are ready to become a human bot, then clearly there are better options out there :)

Why AT/CS and not LK?

LK is too robotic and mind numbing. Plus, finding a Sur Rune or Ohm Rune is like: oh, okay, cool. Finding Mal/Um in AT is like: wow! Finding Ist+ is like: OMG !!!! Much much more excitement :)
Experience, socketables and TC87 items. Pretty much everything that contributes to Diablo being Diablo. Turning yourself into a human bot can be the fastest way, but in my opinion brings no joy and can only lead to getting burned out and bored with this game.
Travincal is a much better choice, at least there is some fight involved, though it's a short one (if you can't clear the council and get back to act 4 in like 30-35s with a non-hork character then I am not sure it's worth it). You also need a very high-end gear, p3 Trav council can apply some serious punch, especially under nasty mods/auras. They drop from TC84, so forget about TC87 items.
Cows can be good for a fast killer, but in my opinion they are also quite boring and irritating (moo moo, moooo!).
​

Is it worth to cube up?

Generally yes, but not when you cube up two more popular runes to get one rarer rune. Examples: 2 Ber into Jah, 2 Lo into Sur, 2 Vex into Ohm, 2 Ist into Gul, 2 Um into Mal = my advise is: don't do that, unless you desperately need that Lo Rune and if you think you won't need those Vex Runes anymore.
Also, I wouldn't care much about runes below like Sol or Io - it will take you ages to cube up to Pul, it takes a lot of time to collect these runes and then stash them, you need to return to the stash more frequently = massive time loss. Also, you need lots of chipped gems, which are very hard to find on Hell difficulty. I personally had tons of Amn runes and no Chipped Amethysts, which are necessary if you want to cube up to Sol Rune, you Amn was kind of "choke point". Actually, I would seriously question collecting charms as well, since upgrading your damage from +2 to +3 won't really change a lot if you already have good charms, but might hurt your run times. The odds for hitting a skiller+life or re-rolling it are similar to hitting a high rune.
​

Best non-LK areas for rune farming

Sur+ runes = Chaos Sanctuary and possibly Worldstone Keep, since after Lo Rune AT loses its primary weapon, urns. WSK can be very dangerous though. Cows if you are a fast killer.
Up to Lo = AT might be better than Chaos, unless you can clear Chaos in no longer than 2x the AT time.
Chaos has one big advantage: high amount of Wraiths, which can be killed over the ground vast majority of the time (in comparison to Arcane Sanctuary, where it's not possible).
Chaos sucks for socketables and for items too (especially TC87) for the very same reason, since a decent part of monsters are Wraiths which won't drop any items actually.
Chaos runs will provide more experience because of Diablo (there won't be any exp penalty). WSK is worse, it takes way longer to get to Baal, CS wins.
AT is a great balance for everything: lots of TC87 stuff, good chance for runes up to Lo, easy monsters, no cold immunes, no Lord de Seis/Archer-like monsters that can one or two-shot you, fast clear time.
CS won't provide as many TC87 (Wraiths drop nothing, Diablo and his 3 seal bosses drop up to TC84), though it provides a good chance for any rune up to Zod, monsters are tougher though, Decrepify curse is a pain and you need to watch out for Lord De Seis - one wrong move + bad combination (fanaticism + Extra Strong + Extra Fast + AMP curse) = and your life bubble can reach the bottom in like 0.2s.
Cows will be probably even better for socketables than AT, but they totally suck for magic items, chances for TC87 items are like 15x smaller.
Cows require more hassle: go to Tristram, get that leg, get tome of TP, clear a huge, wide open area. Whereas AT/CS are much more "restricted" by walls and objects = easier crowd control and navigation.
​
Q: What about the Pits? A: Haven't run them a lot, but I think they will be worse than AT. Hardly any popables, that can drop max up to Vex. Lots of monsters though, yes, but archers who can one/two-shot you are a pain. Entrance is also located way further from the waypoint in comparison to AT (if you get a good map, mine has trap door super close to WP, 2 teleports away).
​
​

Final Verdict

Best rune farming area is the one that will provide the highest kills per minute. Which area is this precisely will depend on your character, build, map you got and your personal preferences.
My answer for my Frenzy barb? Ancient Tunnels wins in almost every single category :)

1000 Ancient Tunnel runs

Oh, I almost forgot about my project :) You already know what kind of runes I acquired, so here are the items: 001-100: barb skiller charm, Natalya's Mark, Gore Rider 101-200: Immortal King's boots from a chest, Thundergod's Vigor 201-300: Mara's, Dragonscale
301-400: Dracul's Grasp, Kira's Guardian, great small charm (+3 max dmg, 20 AR, 16 Life), Bartuc's Cut-Throat, IK Ogre Maul, Carrion Wind 401-500: Rainbow Facet, Tal Rasha's armor
501-600: Arreat's Face - my personal holy grail :) , Tomb Reaver, Templar's Might 601-700: ethereal Berserker Axe, Crown of Ages 701-800: Lightsabre, Amazon skiller +36 life, Reaper's Toll 801-900: Tal Rasha's Lidless Eye, almost perfect Annihilus (19 all stats, 16 all res, +10% exp) 901-1000: Ormus Robes with Blizzard, Death's Web (the rarest drop by far!), 2nd Dragonscale and 6 runs before the end of the project...last piece of IK set - IK Soul Cage :)
I wish you all good luck. May the EV be with you!
​
https://preview.redd.it/syx7g8ukn6y41.png?width=802&format=png&auto=webp&s=2abcb25b71ff2e7c08f1c349f72ff52ec87fb30c
submitted by MatiKosa to diablo2 [link] [comments]

MAME 0.218

MAME 0.218

It’s time for MAME 0.218, the first MAME release of 2020! We’ve added a couple of very interesting alternate versions of systems this month. One is a location test version of NMK’s GunNail, with different stage order, wider player shot patterns, a larger player hitbox, and lots of other differences from the final release. The other is The Last Apostle Puppetshow, an incredibly rare export version of Home Data’s Reikai Doushi. Also significant is a newer version Valadon Automation’s Super Bagman. There’s been enough progress made on Konami’s medal games for a number of them to be considered working, including Buttobi Striker, Dam Dam Boy, Korokoro Pensuke, Shuriken Boy and Yu-Gi-Oh Monster Capsule. Don’t expect too much in terms of gameplay though — they’re essentially gambling games for children.
There are several major computer emulation advances in this release, in completely different areas. Possibly most exciting is the ability to install and run Windows NT on the MIPS Magnum R4000 “Jazz” workstation, with working networking. With the assistance of Ash Wolf, MAME now emulates the Psion Series 5mx PDA. Psion’s EPOC32 operating system is the direct ancestor of the Symbian operating system, that powered a generation of smartphones. IDE and SCSI hard disk support for Acorn 8-bit systems has been added, the latter being one of the components of the BBC Domesday Project system. In PC emulation, Windows 3.1 is now usable with S3 ViRGE accelerated 2D video drivers. F.Ulivi has contributed microcode-level emulation of the iSBC-202 floppy controller for the Intel Intellec MDS-II system, adding 8" floppy disk support.
Of course there are plenty of other improvements and additions, including re-dumps of all the incorrectly dumped GameKing cartridges, disassemblers for PACE, WE32100 and “RipFire” 88000, better Geneve 9640 emulation, and plenty of working software list additions. You can get the source and 64-bit Windows binary packages from the download page (note that 32-bit Windows binaries and “zip-in-zip” source code are no longer supplied).

MAME Testers Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to emulation [link] [comments]

MAME 0.219

MAME 0.219

MAME 0.219 arrives today, just in time for the end of February! This month we’ve got another piece of Nintendo Game & Watch history – Pinball – as well as a quite a few TV games, including Dream Life Superstar, Designer’s World, Jenna Jameson’s Strip Poker, and Champiyon Pinball. The previously-added Care Bears and Piglet’s Special Day TV games are now working, as well as the big-endian version of the MIPS Magnum R4000. As always, the TV games vary enormously in quality, from enjoyable titles, to low-effort games based on licensed intellectual properties, to horrible bootlegs using blatantly copied assets. If music/rhythm misery is your thing, there’s even a particularly bad dance mat game in there.
On the arcade side, there are fixes for a minor but long-standing graphical issue in Capcom’s genre-defining 1942, and also a fairly significant graphical regression in Seibu Kaihatsu’s Raiden Fighters. Speaking of Seibu Kaihatsu, our very own Angelo Salese significantly improved the experience in Good E-Jan, and speaking of graphics fixes, cam900 fixed some corner cases in Data East’s innovative, but little-known, shoot-’em-up Boogie Wings. Software list additions include the Commodore 64 INPUT 64 collection (courtesy of FakeShemp) and the Spanish ZX Spectrum Load’N’Run collection (added by ICEknight). New preliminary CPU cores and disassemblers include IBM ROMP, the NEC 78K family, Samsung KS0164 and SSD Corp’s Xavix 2.
As always, you can get the source and 64-bit Windows binary packages from the download page.

MAME Testers Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to emulation [link] [comments]

QUESTION: How should I customize poker chips?

Hi everyone! I’m new to this subreddit and was seeking some help.
My boyfriend and I’s one-year anniversary is coming up, and quarantine is preventing us from doing anything extravagant. I wanted to make him something creative and personal to make him feel special nonetheless! He’s a big fan of poker. I have light experience in wood working but, seeing that I’m stuck in my apartment and a girl in my early 20’s, I don’t have adequate tools to build a box for a complete poker chips. Instead, I was thinking of making him a custom set of poker chips.
I’ve seen blank 1.5” unfinished wooden chips on Amazon that you can buy in packs of 200. My question lies in how I should best customize these chips. Should I invest in a handheld engraver? Should I stain them? If I paint them, what finish should I be using? I feel overwhelmed with the options, and I’m curious as to what more experienced craftspeople would do!
I should mention I have a background in design, but am wondering what types products would you use to customize poker chips.
Thanks in advance, and I’m grateful to have stumbled upon this creative community!
submitted by Secret-Contest to woodworking [link] [comments]

best poker chip set amazon video

Top 5 Best Poker Chip Set : Poker Chip Set Reviews - YouTube 10 Best COIN & Poker Chip TRICKS! (How to Tutorials) - YouTube 2019 Poker Chip Buying Guide - YouTube 10 Best Poker Chip Sets 2020 - YouTube 6 Best Poker Chip Sets 2016 - YouTube The Greatest Poker Chip 2016 - YouTube 6 Best Poker Chip Sets 2017 - YouTube Best Poker Chip Set 2018 5 Poker Chip Set Reviews! ESPN 300 Poker Chip Set Review ($21.99) - YouTube 2020 Poker Chip Guide - YouTube

Poker Chip Set 500 - Prestige Poker Casino Quality - 12 Gram Chips with Silver Aluminum case High Limit Tournament Set with Free Texas Holdem Calculator 4.5 out of 5 stars 27 #50 Poker Chip Set, Casino Poker Chips Casino 14 Gram Clay Composite Chips x300pcs, with Aluminum Case, Deck of Cards, Dices Texas Holdem Blackjack Gambling Chips,300pcs CDN$ 249.99 CDN$ 249 . 99 CDN$ 39.00 shipping The set includes 500 ceramic chips, two decks of playing cards, and a dealer button. You can find this set on Amazon for around $300. #1 Custom Chip Set From the Poker Depot comes a fully customizable clay chip set. The best poker chip set for you (as it is for everyone who plays poker) is one you can afford. The Trademark Poker Chip Set is the best kit for beginners or for those who just want to make their home parties more fun. The first thing that strikes you with this set is its affordable price and as many as 500 chips enough even for a full ring poker game. Bicycle Carousel Poker Set, 200 2-Gram Poker Chips and 2 Decks of Bicycle Cards by Bicycle. 199. $36.81 $ 36. 81. Arrives: 11 - 13 Jan See all results. Department. Toys & Games; Games & Accessories; Casino Equipment; Poker Chip Cases & Trays; Carousels; Age. 3-4; Amazon Prime. International Shipping; Featured Brands. Bicycle; Avg. Customer Poker Chip Set for Texas Holdem, Blackjack, Gambling with Carrying Case, Cards, Buttons and Dice Style Casino Chips (11.5 gram) by Trademark Poker 4.6 out of 5 stars 3,386 $39.99 $ 39 . 99 Amazon Best Sellers Our most popular products based on sales. Updated hourly. Best Sellers in Poker Chips #1. Fat Cat 11.5 Gram Texas Hold 'em Claytec Poker Chip Set with Aluminum Case, 500 Striped Dice Chips 4.7 out of 5 stars 4,351. $44.64 #2. Da Vinci 50 Clay Composite Dice Striped 11.5 Gram Poker Chips, Choose from 11 Colors 4.7 out Check out the poker chip set from Brybelly on Amazon.com here Smartxchoices, 11.5 gram, 300-count. This poker chip set from Smartxchoices contains all of the basics with the classic design that we all know and love. The chips come in the standard colors: green, blue, black, red, and white. They also have the classic design of stripes and dice. Poker chip case or poker chip holder is lined in styro style felt and poker trip tray exterior is an aluminum blend for lightweight carrying & storage!. Poker chips set includes 300 11.5 gram poker chips, poker chip set includes 50 green chips, 50 black chips, 50 white chips, 50 blue chips and 100 red chips. Buy the Cardinal industries poker chips set on Amazon here 4. Brybelly poker chips set in a wooden carousel case. Coming in at number 4 on the list of poker chips sets, the Brybelly poker chips set complete with a wooden carousel case. The product is one of the very best gifts for any amateur/casual poker player with fantastic gift-focused

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Top 5 Best Poker Chip Set : Poker Chip Set Reviews - YouTube

This is a quick overview of my top poker chip picks January 2019. This is my opinion, which seems to change every month. Please use this as a tool to get sta... CLICK FOR WIKI https://wiki.ezvid.com/best-poker-chip-setsPlease Note: Our choices for this wiki may have changed since we published this review video. Ou... How to do Top 10 Best Coin and Chip Tricks!! Subscribe Now for more How To’s, Pranks, Tricks, Social Experiments and Fun Videos: http://bit.ly/ucmagic and My... CLICK FOR WIKI https://wiki.ezvid.com/best-poker-chip-sets?id=ytdesc Poker Chip Sets Reviewed In This Wiki: Cardinal Industries 200 Piece Poker Kovot 200 ... The Best Poker Chip Set List: 05. 500 Piece Monte Carlo - http://amzn.to/2BVT5DN 04. 500 Ace Casino Poker - http://amzn.to/2DUPcfV 03. Poker Chip Set for Tex... UPDATED RANKING https://wiki.ezvid.com/best-poker-chip-setsDisclaimer: These choices may be out of date. You need to go to wiki.ezvid.com to see the most ... Price changed to $21.99 HERE: http://www.amazon.com/Sportcraft-5-1-36-903-ESPN-Poker-Set/dp/B005VNR158/ref=sr_1_1?ie=UTF8&qid=1364873299&sr=8-1&keywords=espn... Looking for some new poker chips? This might be a good place to start. Each of these chips has their own review so be sure search my channel for more reviews... Most of the time we select wrong type of products without see any reviews. We think top 5 Best Poker Chip Set review video help you for select best one.Subsc... This is an introductory summary of poker chips, originally published on Amazon, but now here in UHD! Jon Hobby summarizes the options for poker chips availab...

best poker chip set amazon

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