Month: September 2019
As we’ve done before, I set up head-to-head matchups and put out a call through FiveThirtyEight social media channels — so, you know, just Americans taking a brief respite from making Harambe jokes during a lunch hour, not exactly the paragon of triple blind scientific research but better than say letting an octopus pick ’em, probably — for people to pick the superior Summer Olympic event.1Rather than split hairs, we broke out individual events within a sport when it was sensible to do so, differentiating between distances, skill sets and individual and team events. I wasn’t totally concerned with weight class differences in combat events. More than 55,000 matchups later, including at least 900 for each event, we have our win percentages:Of the big three groupings — track and field events, swimming and gymnastics — swimming comes out on top when the win percentages of its events are consolidated into an average, but track and field has the most beloved events of all. A few interesting things are going on here: People usually valued team relays more highly than individual events in both swimming and track. Track would have a higher overall win percentage than swimming (64 percent vs. 62 percent), if you dropped out the two very low scoring events of 20 kilometer race walk and 50 kilometer race walk, but we are not going to do that because the IOC must be held to account for its crimes.Outside of the big three, there are a several sports that come off looking very, very good. Volleyball comes out on top, which is hugely unexpected, particularly because indoor volleyball event somehow scored higher than the beach volleyball event. Soccer rolls in at second place overall; it’s no World Cup but, hey, we’ll take what we can get. The triathlon comes in between swimming and track, which makes perfect sense when you think about it for a couple seconds.Then of course you have rugby, an addition this year but the closest we’re ever going to get to actual American football in the games. (The IOC simply refers to the sport as “rugby,” but it’s the seven-man version, not the 15-man one, and that’s what we’re talking about here.) And apparently there’s a sleeper audience out there for water polo, the blood sport, which somehow comes out on top of basketball.On the other hand, the people have no patience for equestrian sports, sailing, golf or any other one-percenter hobbies the IOC is presumably taking under consideration for inclusion, like fox hunting or fleecing the middle class or getting thrown out of Choate.Respondents also have low opinions of synchronized swimming and rhythmic gymnastics, because apparently there’s just too much goddamn beauty in the world and all of y’all would just hate to see a little more art on this Earth, you ingrates.Still, this allows us to get solid determinations of sport vs.sport rankings: In terms of moving somewhere with alacrity in a body of water, swimming beats out rowing which beats canoeing which beats the crap out of sailing in terms of Olympic integrity. Fun fact: This is also the opposite of the real world ranking of “If you dropped me in the Guanabara Bay how would I like to leave there.”In keeping with the “Great Teenage Girl theory of Olympic history,” the individual all-around artistic gymnastics competition is the most prized of all the gymnastics events, followed by the uneven bars, the balance beam and parallel bars. The worst event in gymnastics, we can all agree, is the horizontal bar.In terms of combat, Greco-Roman wrestling beats boxing, which beats freestyle wrestling, which beats judo, which beats fencing, which beats taekwondo, all of which are considered inferior to competitive diving. Consider that the next time someone wants to take it out back to the parking lot.As a rule, with rowing, the more people you have in your boat the better the event.In track and swimming, shorter distances are vastly preferable to longer ones. On the field side, the long jump and high jump are better than the pole vault and triple jump. If you’re going to throw something, make it a javelin. A discus will suffice in the absence of a javelin; a shot and hammer are right out.And if you really must be caught on a horse, it had better be jumping, and you had better be doing it alone. But in general, leave the horses out of it. The Olympics are for people stuff. We’re on the ground in Rio covering the 2016 Summer Olympics. Check out all our coverage here.The Olympic Games are finally here. The modern incarnation began back in 1896, but the Olympic tradition harkens back to the ancient Greek festival where a massive municipal infrastructure boondoggle was offered unto the gods as a tribute.Many of the sports appearing at the Summer Games have storied and truly ancient histories, and not just the obvious ones like the discus and javelin events. The race Herodotus presumably called “βμχ” is now honored with the Olympic event of bicycle motocross racing. Who could forget the ancient story of Achilles gaining victory over the Trojan Prince Hector in a particularly devastating table tennis bout. And most of all, while Pheidippides’ run following the Greek victory at the battle of Marathon gets all the glory, today we also commemorate in the Olympic Games the tenacious if less celebrated efforts of an unnamed Achaean soldier after the Battle of 50 Kilometer Race Walk.Alright, I’ll just say it: Some Olympic events are better than others. Some are way better than others. And I wanted to figure out which are really the best.
Ray Lewis surprised his Baltimore Ravens’ teammates with a visit Friday at practice for the first time after suffering torn triceps about a month ago. And he left the impression that he will not retire.Lewis, perhaps the most dominant defensive player in the last decade, greeted teammates with hugs and chatted with head coach John Harbaugh on the practice field.“It’s truly great to be back with my teammates,” Lewis said in a statement. “I’ve really missed these guys and the feel of being around the team and in the locker room. I am focused on rehabbing and getting my arm and body as strong as they can be. I will speak in person when I know a little more about my progress. I’m working hard and looking forward to coming back and helping this team. But right now, the focus should be on the guys playing, and I’ll be the biggest cheerleader I can be for them.”It was unclear if Lewis was referring to returning this seasoThe Ravens placed Lewis on injured reserve with a designation to return, which means he would be eligible to come back Dec. 12. However, based on the general recovery period for a torn triceps, it’s highly unlikely Lewis would be healthy enough to play in the regular season and most of the postseason. His best shot is playing in the Super Bowl, should the Ravens make it that far.There have been questions whether Lewis would return for an 18th season. He turns 38 in May.Before the injury, Lewis had hinted that he was close to calling it quits. Last summer, he said he couldn’t see playing past the age of 37. And Lewis has talked about his desire to watch his son, Ray Lewis III, who will be a freshman running back at the University of Miami next fall.
Before any games have tipped off, the NCAA tournament already has an almost surefire winner: Warren Buffett.As you’ve likely heard, the billionaire investor is backing a prize of $1 billion to anyone who correctly picks the winner of every game in the men’s college basketball tournament. Announced on Jan. 21, the contest grabbed far more attention than any perfect bracket competition ever had before.No forecast, even FiveThirtyEight’s, is a sure thing. But here’s one prediction we can make with almost complete certainty: Buffett won’t have to pay out. Mathematicians disagree on precisely how difficult it is to make a perfect bracket, but they agree the chance is so small that it’s not worth quibbling over.Big money has been used to spark innovation before. That’s the idea behind the X Prize for advances in energy, exploration and so on. The difference there is that inventors actually have a real shot at developing clean, renewable fuels. Even a $1 trillion come-on can’t inspire an unblemished NCAA bracket. No sum of money can beat the math.“The bottom line,” said DePaul University mathematician Jeffrey Bergen, “is that Mr. Buffett has very little to worry about.”To our knowledge, no one has ever produced a perfect bracket, the three-minute mile of office pools. Perhaps one is buried in the boneyard of photocopied, hand-filled brackets of yesteryear. Internet-era contests are easier to track. Against the standard of perfection, more than 30 million ESPN brackets have failed, one by one, in the past 16 years, according to a spokeswoman. Over the last two years, no CBSSports.com bracket remained perfect through the second day of the tournament, a spokeswoman said. Yahoo’s entrant who came closest to perfection got 58 of 63 games right in 2007, according to a spokesman; last year no one picked more than 50 games correctly at either CBS or Yahoo. (Yahoo is partnering with Buffett’s perfect bracket contest.)Correctly predicting any one game in the tournament may be only a bit easier than calling a coin flip — not very good odds. Predicting all 63 games is nearly impossible. (Quicken Loans, the sponsor of the Buffett-insured offer, is omitting the four play-in games for its bracket contest, conforming to office-pool norms.)Several mathematicians offered explanations for this difficulty, ranging from the abstract to the concrete. Bergen, in a 2012 YouTube video that resurfaced after Buffett offered his billion, pointed out that predicting 63 coin flips correctly was a 1-in-9.2-quintillion proposition.1A quintillion is a billion billion, or one followed by 18 zeros. That’s because the probability of getting 63 out of 63 right is the product of the probability of getting each one right, which for a coin flip is 50 percent.Most games aren’t coin flips, though. A No. 1 seed has never lost to a No. 16, which makes those round-of-64 games about as close to sure things as big sporting events get. Make a few assumptions about your chances of calling each game right and you can get estimates. They’ll be very sensitive to the assumptions, though, because they’re being multiplied together 63 times.2Suppose you’re building a model to enter the Buffett bonanza. If your chance of being right was 70 percent, on average, for every game, then you’d have a 1-in-5.7-billion chance of a perfect bracket.But your chance of being right isn’t the same for every game. Some games will be more or less sure — for instance, some first-round games pit favorites against the last teams in, while Final Four games should be more competitive. So suppose you know the winner of 21 games with 90 percent certainty, another 21 at 70 percent and the last 21 at 50 percent — coin flips. The average probability remains 70 percent, but now your chance at Buffett’s prize has plummeted to 1 in 34.3 billion.This follows logically from some basic algebra. Call your average probability of predicting a game correctly x. Suppose you’re predicting two games. In one scenario, you have an x chance of predicting each game, and an x^2 chance of calling both right. In an alternative scenario, you have an (x+y) chance of calling the first game right, where y is some number less than x and an (x-y) chance of calling the second. Remember the product of (x+y) and (x-y) is x^2-y^2. That is your chance of getting both games right, and it is less than x^2 — even though your average probability is the same for each game.This has implications for your March Madness model. However refined it is, its predictions will contain some uncertainty. It’s based on finite data about teams full of college students who might get overwhelmed by the pressure or, for that matter, get injured. So even if your fancy model somehow spits out probabilities of 70 percent for each game, some might be 68 percent, or 72, or even 50 or 90. And so the overall probability you calculate of getting the whole thing right will probably overstate it, by the same (x+y)*(x-y) logic above, even if your confidence in getting any one game right is, on average, well founded.The challenge is so enormous that some scientists consulted for this article shrank from providing detailed calculations in their estimates of the probability of choosing a perfect bracket. Their guesses ranged from about 1 in 5 billion to 1 in 135 billion. In response to a journalist’s query, University of Minnesota biostatistician Bradley P. Carlin used 1 in 128 billion, which is “just the opinion of one expert I found online,” he said.That expert is Bergen, who cites the estimate in his video but doesn’t explain it. At my request, he provided his assumptions: that the probability of calling a first-round game correctly ranges from 51 percent for the No. 8 vs. No. 9 game to 100 percent for the No. 1 vs. No. 16; and that second-round games can be called with 65 percent accuracy. The figures are 60 percent for Sweet Sixteen games and 50 percent for every game from the Elite Eight through the final.“This is based on some simplifying assumptions and could be tweaked based on historical data, but is probably not unreasonable,” Bergen said. (He has since posted a new video explaining his calculation.)Bergen’s model, and his peers’ calculations, don’t incorporate any information about the actual players and teams that give March Madness its flesh and blood. A more empirical way to fill out a bracket is to use some sort of team power rating to calculate the probability of each game’s outcome, then choose the most likely set of 63 outcomes. A 2006 attempt using Ken Pomeroy’s ratings found the most likely bracket had a 1-in-722 billion chance of being right, and in 2011 the figure was 1 in 83 billion.Suppose, though, that the $1-billion offer lets a thousand Ken Pomeroys bloom. Perhaps some previously unknown analyst solves college basketball’s unsolvable riddle. That’s how the Buffett offer looks through the X Prize lens.The trouble is, sports predictions can’t be solved the way space exploration or fuel economy can. When athletes, officials and physics collide, no one can be sure what will happen beyond some upper limit of probability. And while there’s room in all sports to improve on our current understanding, college hoops is a particularly tricky sport to forecast. Players have just a few years of track record, at most. Most pairs of tournament teams haven’t played each other, or played at all in the neutral host venues.“It’s probably not the best data set to use to make real improvements in statistical modeling,” said Mark Glickman, a research professor of health policy and management at Boston University who holds a doctorate in statistics. And even if someone makes a major breakthrough in predicting college hoops, there’s more reliable money to be made in nightly Las Vegas wagers than in lifting your expected yield from a perfect bracket contest to, say, $5 from 50 cents.March Madness sits in a sweet spot of prediction contests: It’s hard enough to yield no winner yet popular and tempting enough to produce lots of entrants and publicity. The World Cup has greater global popularity and roughly the same number of games but lacks the simple, single-elimination bracket structure. Tennis Grand Slams share the brackets but lack the NCAA Tournament’s U.S. popularity. Besides, they’re too tough: With fields twice as large (and with about three times as many near-toss-up matches as in an NCAA tournament’s round of 64), choosing the first round is about as tough as winning the Buffett contest. NFL survivor pools are too easy: Roughly one out of every 2,600 entries in ESPN’s Eliminator Challenge, 166 in all, made it through to the end of last year’s regular season.No matter how you slice it, any one March Madness entrant in a perfect bracket contest has next to no chance of winning. But if you open the gates to up to 15 million entries, with an option to allow more in — the terms of the Quicken offer — the risk may start to seem real. If all 15 million entries are different, the probability is roughly the number of entries multiplied by the probability of any one entry winning.3More precisely, if each entry has the same chance of perfection, p, and there are x different entries, then the chance that there is a successful entry is one minus the chance that there is no successful entry. The amount to be subtracted from one is, in turn, the product of the probability that each entry is wrong, which is (1-p) multiplied together x times. Put it all together, and you get 1 – (1-p)^x. (1-p)^x is a polynomial with many terms. When p is very low, the first two are far greater than all the rest together, so (1-p)^x simplifies to 1-p*x, and so 1 – (1-p)^x is roughly p*x. Or, going back to our March Madness case, roughly 15 million times whatever we decide p is. If it’s 1 in 135 billion, this calculation implies there’s a 1-in-9,000 chance of a perfect bracket.But that last “if” is a big one. It’s fun to mess around with wild upset picks in a private contest for bragging rights with your friends. When entering a competition for $1 billion, though, why bother choosing a bracket with anything less than the best possible chance of winning?Mathematicians advise doing whatever it takes to stand out from the chalk crowd — those office-pool pests who rank highly simply by choosing higher seeds to win every game — while maintaining the same order of magnitude of probability of winning. (Duplicating other entrants’ brackets — or FiveThirtyEight’s, or any other widely published one — could be costly, since the $1 billion will be split among winners.) The obvious thing is to exploit questionable seeding decisions: Last year, Pomeroy rated eighth-seed Pitt above many higher seeds. More subtly, you could choose an unlikely outcome that opens up the draw and makes subsequent games easier to call. “Depending on how you assess prediction odds, the ‘top-ranked always wins’ bracket is not necessarily the most likely,” said John Pike, a mathematician at Cornell University.But there aren’t 15 million especially likely variations on the theme of choosing a bracket with an eye to perfection. Statistician Scott Berry, president of Scott Berry Consultants, figures that a bracket picking all favorites has a 1-in-85-billion chance of being correct. Depending on how the 15 million entrants to Buffett’s contest choose, balancing the desire for a unique bracket with one for reasonable picks, Berry figures the collective probability of winning the jackpot ranges from one in 42,370 to one in 3.3 million.(At Grantland, Ed Feng calculated an average probability for a perfect bracket in each of the last 10 years — if Buffett’s contest had run each year — of 1 in 590,000, which Feng calls “incredibly optimistic.”)Damien Sutevski is trying to boost those collective odds. Sutevski, a graduate student at UCLA who studies fusion engineering, created the website Take Buffett’s Billion to coordinate entries. People who sign up are assigned brackets that are likely without ever being duplicative. They agree to donate any winnings to charities (the Immunity Project and Habitat for Humanity).“I thought it’d be fun to game Buffett’s challenge by pooling people together,” Sutevski said. Based on simulations by Michael Beuoy, a sports analyst, he figures that the average probability of the 15 million most likely brackets is a little under half the probability of the single most likely one. So if they can enlist all 15 million entrants in the contest to join forces, they can drive the odds down to about one in 10,000, and boost their odds of winning some of the $2 million set aside for the best imperfect brackets. As of Monday afternoon, 9,061 people had signed up.The more duplicate ballots Buffett and Quicken get, the safer they can feel. In fact, their biggest risk isn’t from an NCAA Nostradamus but from a modern-day Kevin Mitnick or Cornelious Kelleher. A hacker or a match-fixer has the surest, though least legal, way to riches in a bracket contest. “The odds of someone winning the prize through fraud,” Pomeroy said, “are better than the odds of someone winning the prize honestly.”That doesn’t mean that if somehow a forecaster defies the odds and wins the contest he or she is definitely a cheat — only that it’s the most likely explanation when simply predicting all 63 games is so very, very unlikely.“There are two big risks in this,” Buffett told ESPN’s Rick Reilly. “One, somebody does it. Two, somebody tries to screw us.”
That still leaves the Big Four in a much stronger state than the prior ruling class ever reached.2None of the three men who preceded Federer, Nadal and Djokovic as new world No. 1s ever had a year as good as each one is on pace to have this year, even though it’s a down year for tennis titans. The trio — Andy Roddick, Juan Carlos Ferrero and Lleyton Hewitt — are Federer’s contemporaries, but they never unseated him the way the much younger trio of Nadal, Djokovic and Murray have.The five younger upstarts — Cilic and Nishikori, plus Grigor Dimitrov, Ernests Gulbis and Milos Raonic, who are between age 23 and 26 and reached their first Slam semis this year — have mostly been defined by their arrested tennis development. They’ve shed the previously apt Lost Boys title with career years this season. But they’re more of a Medium Five or Next Five than a Fab Five, though at least, unlike that Michigan quintet, one of these five will win a major title in a Monday championship.There wasn’t an obvious name for the group until Cilic perfectly described it after his near-perfect match Saturday. In his press conference, Cilic referred three times to the sport’s “second line.”“The guys there are from second line, are moving closer and they are more often at the later stage of the tournament,” Cilic said. “They are going to get only better; they’re not going to get worse.”Cilic was including Australian Open champ Wawrinka in his list, but Wawrinka, 29, is older than three of the Big Four. For our purposes, we’re not going to include him so that the “second line” is a separate generation from the current stars, who are between ages 27 and 33.There is one major name missing from the second line’s roster: Juan Martin del Potro, who turns 26 this month. He and the second line are the six youngest men ranked in the Top 20. Even Monday’s champion probably won’t finish this year with a season as good as del Potro’s best. In 2009, the year he turned 21, del Potro won the U.S. Open, came within a set of the French Open final and reached the quarterfinals of six Masters tournaments.Injuries have kept him from maintaining that level, and from playing any tennis for the last six months. But already del Potro has achieved in his career more than the quintet of his peers will have achieved after Monday’s final: one major title; three Masters finals (the other five have two total); and seven titles at the 500 level, the next tier down from Masters events (the other five have five, and three of those belong to Nishikori). Before Monday, del Potro was the only man who is now under 27 and had played in a Grand Slam final. Federer had won 12 Grand Slam titles before turning 27.Adjusting for age shows how much the second line lags the first, even in a career year. The most accomplished four of the bunch — del Potro, Cilic, Raonic and Nishikori — have won 24 percent of their maximum possible total of ranking points at the Masters and Grand Slam events this season. The Big Four, at roughly the same average age in 2010, won 68 percent. At age 20, the Big Four won 53 percent to the second line’s 5 percent at the same age. And this year, all of the Big Four could finish ahead of all five of the second line in the rankings.So the second line isn’t the generation we need to take over men’s tennis. But it’s the one we have — at least until the generation of Wimbledon quarterfinalist Nick Kyrgios and other promising teenagers launches its challenge. And the second line is playing its best tennis this season. Heading into the U.S. Open, Dimitrov, Nishikori and Raonic all were on pace for career highs in dominance ratio (DR), the ratio of returning points win percentage to opponent returning points win percentage — a good marker of overall level. And Cilic and Gulbis were serving better than ever, with career highs in ace percentage and first-serve win percentage.Through the lens of the past week, Nishikori looks like the most impressive member of the second line. He beat three top-five seeds in his last three matches at the Open. But his DR was below 1 in his last two wins, meaning he won them with little margin.Cilic, by contrast, straight-setted two top-six seeds in his last two matches, and was forced into just one tiebreaker. He has also been more durable than Nishikori has during their careers. The two youngest members of the group, Dimitrov and Raonic, have stayed mostly healthy, too, and have climbed the most steadily and quickly.One of those three or del Potro probably will finish with the best career of the second line’s generation. Whoever does will be hard pressed even to match the achievements of Murray. He’s by far the weakest member of the Big Four, an oligarchy that remains dominant even in its decline. The reigning oligarchs of men’s tennis — Roger Federer, Rafael Nadal, Novak Djokovic and Andy Murray — have devolved a smidgen of power to the sport’s second line.For six years, the Big Four have ruled the sport, hoarding its biggest titles and topping the rankings. This year, five younger men have broken through, most dramatically on Saturday at the U.S. Open. First Kei Nishikori stunned world No. 1 Djokovic in four sets. Then Marin Cilic straight-setted five-time Open champ Federer. Nishikori and Cilic will play in Monday’s final, the first Grand Slam final for each man and the first without a member of the Big Four since 2005.Federer lightly applauded his younger rivals’ modest achievements in his post-match press conference Saturday. After congratulating Cilic for his great play, Federer called it “definitely refreshing to some extent” to have new names in a Grand Slam final, and added, “I hope they can play a good final.”Federer pointed out that any tennis writers who downgraded the Big Four’s stock when Stan Wawrinka won in Australia in January had to explain the finals of this year’s French Open and Wimbledon, which featured exclusively himself, Nadal and Djokovic. “But this is another chance for you guys, you know,” Federer told the room full of journalists. “So you should write what you want. I don’t think so, but … .”Federer is both right and wrong. The Big Four really have been slipping. That can be hard to spot because each player has had a complicated arc over the last few seasons. Djokovic gained, lost and regained the No. 1 ranking, with Nadal and Federer each holding it for a spell. Federer has rebounded swiftly from a mediocre 2013. Murray has dropped fast after winning two Grand Slam titles and an Olympic gold medal over a 12-month run that ended last summer.Group the four together, though, and the decline is more apparent. In 2011, they won an absurd 84 percent of their maximum possible total of ranking points at the 13 annual tournaments that bring together the world’s best male players: the Masters and Grand Slam events. That share has fallen steadily each year since, to 66 percent so far this year. Some regression to the mean was inevitable, but the Big Four’s grip on the sport has fallen below the mean, to its lowest level since 2006, according to data compiled from the stats site Tennis Abstract.1The analysis is based on the current distribution of ranking points at these events — 2,000 points for winning a Grand Slam title, 1,200 for a semifinal, and so on. The distribution has shifted slightly over the years. Using a constant distribution weights a U.S. Open quarterfinal berth the same whether it was earned in 2004 or 2014.
It’s a challenge not necessarily because the selection committee is inherently unpredictable. Most of the time, several of the playoff participants turn out to be fairly obvious, and our model has correctly predicted 11 of 12 playoff participants in the three years of its existence so far.2Based on game results through the final week of the regular season before the committee released its final rankings.The goal of a statistical model, however, is to represent events in a formal, mathematical way, and ideally, you’d like to be able to do that with a few relatively simple mathematical functions. Simpler is usually better when it comes to model-building. That doesn’t really work in the case of the selection committee, however. We finally have a reasonable amount of data to work with — 2017 will be the fourth year of the playoff. And what we’ve found is that even though our model can do a reasonably good job of anticipating the committee’s behavior, it has to account for the group behaving in somewhat complicated ways.We discovered in 2014, for example — when the committee excluded TCU from the playoff despite the team holding the No. 3 spot in the committee’s penultimate rankings — that it isn’t always consistent from week to week. Instead, it can partly re-evaluate the evidence as it goes. For example, if the committee has an 8-0 team ranked behind a 7-1 team, there’s a reasonable chance that the 8-0 team will leapfrog the other in the next set of rankings even if both teams win their next game in equally impressive fashion. That’s because the committee defaults toward looking mostly at wins and losses among power conference teams while putting some emphasis on strength of schedule and less on margin of victory or “game control.” Therefore, our model does the same thing, based on a version of Elo ratings that attempts to mimic the committee’s behavior, along with a separate formula based simply on wins and losses. (For a more formal description of how our model works, see here.)We’ve added other wrinkles over the years. Before the 2015 season, for example, we added a bonus for teams that win their conference championships, since the committee explicitly says that it accounts for conference championships in its rankings (although exactly how much it weights them is difficult to say).3Determining how much a conference championship matters is tricky because a team that wins a championship game has a lot of other things going for it — for instance, by virtue of winning its conference’s championship game, a team gets an additional head-to-head win against another strong team, something the committee (and our model) already value highly. In the three years of the selection committee so far, it doesn’t appear that many decisions have come down to whether a team won its championship or not. Still, our testing suggests that the committee probably does reward winning championships, at least for teams in power conferences. And late last year, we added an adjustment for head-to-head results, another factor that the committee explicitly says it considers. The committee has been a bit more consistent about applying this criterion, according to our testing. If two teams have roughly equal résumés but one of them won a head-to-head matchup earlier in the season (say, Oklahoma over Ohio State), it’s a reasonably safe bet that the winner will end up ranked higher.Still, there are no guarantees. Our college football forecasts — like all of our forecasts at FiveThirtyEight — are probabilistic. Not only do we account for the uncertainty in the results of the games themselves, but also the error in how accurately we can predict the committee’s ratings. I spent some time this week evaluating our model’s published forecasts from 2014 to 2016 and found that they were pretty well-calibrated. That is to say, teams that are given a 60 percent chance of making the playoff will actually make the playoff about six out of 10 times and fail to do so about four out of 10 times over the long run. Because the potential for error is greater the further you are from the playoff, uncertainty is higher the earlier you are in the regular season. As of the launch of our forecast in early October, for example, as many as 15 or 20 teams still belong in the playoff “conversation.” That number will gradually be whittled down — probably to around five to seven teams before the committee releases its final rankings.We’ve made a few additional changes in preparation for launch this year, which I’ll briefly describe here:First, we’re using the AP poll as a proxy for the committee’s rankings until the committee releases its first set of rankings on Oct. 31. This change has allowed us to launch our forecast earlier than in past seasons. (We’d previously waited until the committee’s first rankings were out.) Our model builds in additional uncertainty while the AP poll is being used, to account for the fact that the committee, which is made up mostly of former coaches and athletic directors, doesn’t size up the teams in quite the same way that the media voters in the AP poll do.Second, game-by-game forecasts are now based on a combination of FPI ratings and committee (or AP) rankings, instead of solely FPI. We think FPI is a really good system, and we’re not saying that just because it was developed by our ESPN colleagues — it’s done an excellent job of predicting games over the past three years. In our testing this year, however, we found that accounting for the committee’s rankings (or the AP’s rankings before the committee’s rankings are available) contributes some predictive power (in addition to FPI). So game predictions are now based 75 percent on FPI and 25 percent on the rankings.4Since the committee ranks only the top 25 teams, we estimate how they rate the remaining 105 FBS teams based on our Elo ratings.And, finally, our system now gives teams from power conferences more advantages, because that’s how human voters tend to see them. We’ve calculated our Elo ratings back to the 1988 college football season. Between each season, ratings are reverted partly to the mean to account for roster turnover and so forth. In a change this year, teams are now reverted to the mean of all teams in their conference, rather than to the mean of all FBS teams. Thus, teams from power conferences — especially the SEC — start out with a higher default rating.5To be more precise, our model treats conferences as existing along a spectrum, rather than in binary groups of “power” and “minor” conferences. For instance, the American Athletic Conference — which has two teams in the AP top 25 — is more highly rated than the Sun Belt Conference. This both yields more accurate predictions of game results and better mimics how committee and AP voters rank the teams. For better or worse, teams from non-power conferences (except Notre Dame) rarely got the benefit of the doubt under the old BCS system, and that’s been the case under the selection committee as well. In addition, we’ve made the conference championship bonus larger for teams from well-rated conferences; this also improves predictive accuracy.Our forecasts will update at the end of each game, as well as when new AP rankings or new committee rankings are released. We hope you’ll have fun following the season with us. FiveThirtyEight’s College Football Playoff forecast model is in some ways both my most favorite and my least favorite of the many statistical models we publish. That’s because, instead of trying to predict the games themselves — we mostly1In previous years, the game forecasts were based entirely on FPI. This year, they’re based mostly on FPI instead — see below for more detail. defer to ESPN’s Football Power Index for that — we try to predict the behavior of the small group of human beings who make up the playoff selection committee. This is a lot of “fun,” but also quite a challenge.
Despite having the week off, Ohio State moved up two spots in the BCS rankings, to No. 9 from No. 11. The Buckeyes benefited from losses by Utah, Alabama and Oklahoma. Oregon maintained its No. 1 ranking, while Auburn stayed at No. 2. TCU, which beat Utah 47-7, held onto its spot at No. 3, and Boise State stayed at No. 4. With its 24-21 win over defending BCS Champion Alabama, LSU moved up to No. 5. Stanford made perhaps the most significant leap, rising seven spots to No. 6. The Cardinal beat Arizona 42-17 on Saturday. Wisconsin, the highest-ranked Big Ten team, moved up to No. 7. Nebraska edged Iowa State 31-30 after the Cyclones failed to convert what would have been a game-winning two-point conversion. The Cornhuskers slipped one spot to No. 8. OSU hosts Penn State at 3:30 p.m. Saturday. The ESPN College GameDay crew will broadcast its show live from Columbus on Saturday morning.
Jared Sullinger scored 554 points and collected 279 rebounds during his senior year at Northland High School. He also weighed 290 pounds. “I kind of knew coming into Ohio State I was going to have to drop weight for the style of play coach (Thad) Matta plays,” the 6-foot, 9-inch Sullinger said. He did just that, cutting 20 pounds before his first game as a Buckeye on Nov. 12. Sullinger’s weight now fluctuates between 265 and 270 pounds, he said. Dropping the weight had nothing to do with a change in diet but “was all exercise,” Sullinger said. His workout regime is largely left to the discretion of associate strength and conditioning coach Dave Richardson. “Coach Rich is the man who gets us in shape and gets our bodies right for the season,” senior guard David Lighty said. “It’s up to him, and he’s the man who got us to where we are right now.” The Buckeyes are 6-0 and Sullinger is averaging 14.5 points and 9.3 rebounds a game. Sullinger attributes his success so far to the ramped-up training. “With the sand pit running and the miles … we run, it was like a piece of cake out there” on the court, Sullinger said. Besides the runs and sand pit work, the players run stairs at the Schottenstein Center to prepare for the season, Lighty said. Sullinger also said his defensive stance has been more consistent because of his work on technique in the sand pit, he said. Despite the improvement on that side of the ball, Matta still expects more. “There’s still another gear in him on the defensive side that we got to work on,” Matta said. Though he knows the hard work isn’t over, Sullinger said Matta is relatively happy with his conditioning. “He’s been confident enough to play me down the stretch when the game gets tough,” Sullinger said. “Last game (against Florida State), he played me 36 minutes, which I was really surprised because it was a really up-and-down game.” Though he is happy with his weight, Sullinger said it has always been an issue. “It’s been all my life,” he said. “I’ve always been a heavyset kid.” Matta has also been aware of the issue for a long time. The first time he told Sullinger to lose weight, “he was at a camp in fifth grade,” he said, laughing. Joking aside, Matta is not too concerned about Sullinger’s weight, but rather his ability to play in the system. “If he’s at 270 (pounds) and is physically in shape and can play at the speed and pace we want to play at, I’m fine with it,” Matta said. Although he has been productive on the court, Sullinger knows his conditioning must get better. “If you’re not losing weight,” he said, “you’re really not working hard.”
Members of the OSU football team enter Ohio Stadium before a game against Western Michigan on September 26. OSU won 38-12. Credit: Muyao Shen / Asst. Photo Editor Redshirt junior quarterback Cardale Jones snapped out of his early-season funk with a career-high 288 yards passing, leading No. 1 Ohio State to a commanding 38-12 win over Western Michigan on Saturday.The OSU offense was under fire by many during the week leading up to the game after putting up just 298 yards of offense against Northern Illinois in Week 3, but the Buckeyes doused the flames with 511 yards against the Broncos.“We had to come back and we all had to get engaged with each other all over again,” redshirt sophomore H-back Jalin Marshall said. “We practiced hard for the last four weeks, and this week it really paid off. We struggled, but I think we came up today and we showed everyone that we are a good offense.”OSU (4-0) came out of the gates aggressively, forcing a WMU (1-3) punt three plays in, and then taking just three plays of its own to get on the scoreboard. A run of 26 yards by junior running back Ezekiel Elliott brought the ball to the 38-yard line, where Jones connected with redshirt junior receiver Michael Thomas for a score.The touchdown pass was Jones’ first since the Buckeyes’ opening game at Virginia Tech, and Thomas’ third of the year.The Bronco offense showed resilience on their second drive, traveling 66 yards in 16 plays. However, the effort went for nothing as a 37-yard field-goal try was knocked down by OSU redshirt junior safety Tyvis Powell to keep the score at 7-0.“I couldn’t believe it,” Powell said about his block. “The stadium erupted, you get to hear that noise, it was a good moment.”The Buckeyes had trouble stopping WMU’s rushing attack in the first quarter, which was led by seven carries from sophomore Jarvion Franklin and redshirt freshman Jamauri Bogan, who ran for 48 and 39 yards respectively. The WMU run game was aided by the Buckeyes’ strategy to only stand six tacklers across from the offensive line.“They schemed us up a little bit and showed us some things that we haven’t seen,” sophomore linebacker Raekwon McMillan said.The Broncos’ third drive went similarly to their second, this time a 14-play, 62-yard drive. It also had a similar ending as a holding call set up a 47-yard field-goal attempt that did not have enough distance.Jones and the Buckeyes stuck mostly to their air on their next drive, as the Cleveland native completed all three of his pass attempts for 58 yards, culminating with a 37-yard downfield pass to an open Marshall for the score.The touchdown catch was Marshall’s first of the season after pulling in six in 2014.“It just felt good to be back in the end zone again,” Marshall said. “I’m feeling more comfortable now, new position on the offense, playing a new role, but I feel like I’m getting better every week.”The Broncos quickly responded on the following drive as redshirt junior Zach Terrell found redshirt junior receiver Daniel Braverman along the sideline for a 55-yard touchdown to put WMU on the board. The nightmare day for senior kicker Andrew Haldeman continued after the score, though, as his extra-point attempt hit the upright to keep the score at 14-6.After a three-and-out to each side, Jones continued his aerial assault on the first play of the drive with a 40-yard pass to sophomore H-back Curtis Samuel. The drive stalled from there, but a 30-yard field goal by redshirt senior Jack Willoughby made the score 17-6.OSU then added to that lead on the first play of WMU’s drive, when Terrell lofted it across the middle directly into the hands of OSU senior defensive tackle Adolphus Washington for a 20-yard interception-return touchdown.The 18-point halftime lead was OSU’s largest of the season, eclipsing the 14-0 lead against Hawaii. Jones was 13-of-19 for 226 yards at the break, while Elliott carried the ball six times for 46 yards.Terrell was 12-of-22 in the first half for the Broncos, with all 12 completions going to Braverman (seven) or junior receiver Corey Davis (five).The offense picked up where it left off to start the second half, using 14 plays to travel 75 yards and make it 31-6. Elliott got in on the scoring with a six-yard rush, his 27th rushing yard of the drive on his fifth carry. The St. Louis native also brought the crowd of 106,123 to its feet early in the drive with a leap over a WMU defender, his second hurdle of the day.“I’m tired of taking those shots to the legs, those bruises, so I decided to go up top a couple times,” Elliott said.Though the game was safely in hand for the Buckeyes at that point, Terrell led a 12-play scoring drive for the Broncos, ending with a one-yard pass to sophomore tight end Jeremiah Mullinax — the first reception of the game for WMU not by Braverman or Davis. The Broncos went for two points, but Washington and junior defensive end Joey Bosa combined on a sack of Terrell to keep the score 31-12.A 40-yard touchdown run on a draw play by Samuel midway through the fourth quarter extended OSU’s lead to 38-12. Samuel had six rushing touchdowns as a true freshman last season, but Saturday’s score was his first on the ground in 2015. He did have one receiving touchdown in OSU’s opener at Virginia Tech.Jones finished 19-of-33 for 288 yards, eclipsing his previous high of 257 set in last year’s Big Ten Championship game against Wisconsin. He also threw for two touchdowns and one interception.Despite the career-best yardage mark, Meyer said he still believes the quarterback has work to do.“Overall, I thought Cardale played OK,” Meyer said. “He threw for 288 (yards). I still give him the okay because we have high expectations and a couple turnovers.”Marshall agreed that the team was happy with the offensive performance, but it could have easily been a bigger day.“We could have played significantly better,” Marshall said. “I think we could’ve scored 50 points.”Still, Jones said the game stood as a big step forward for him after a start to the season that saw him getting pulled from each of OSU’s first two home games.“I definitely feel more comfortable,” Jones said. “We’re starting to get on the same page as not just receivers or offensive line or things like that. I think everyone felt way more comfortable today.”Jones’ counterpart Terrell equaled his two touchdowns and one interception, completing 18 of 33 passes for 169 yards. Braverman led the way with 123 yards receiving on 10 catches.Elliott compiled 124 yards on the ground, completing his ninth consecutive game with over 100 yards rushing. He also added three catches for 29 yards.The Buckeyes are set to hit the road for their next game, traveling to Bloomington, Indiana, to take on the Indiana Hoosiers on Oct. 3. Kickoff is scheduled for 3:30 p.m.
Red Shirt junior goalie Sean Romeo (30) dives for a save in the shutout against Michigan tonight Jan. 26, 2018 at the Schottenstein Center in Columbus, OH. Credit: Ethan Clewell | For The LanternThe No. 6 Ohio State men’s hockey team (19-7-4, 12-7-1-0 Big Ten) split a weekend road series against No. 1 Notre Dame (22-6-2, 16-3-1-1 Big Ten) to avoid a season sweep. The Buckeyes lost 2-1 Friday night, then bounced back for a 5-1 win Saturday.Game One Notre Dame sophomore goaltender Cale Morris made 31 saves on 32 shots in a 2-1 win against Ohio State Friday to capture the Irish’s first regular-season Big Ten title. Special teams kept the Buckeyes in the game, getting their only goal on the power play, putting them 1-for-4 on the night. The Buckeyes top-ranked penalty kill went an impressive 6-for-6 on the night, including killing two 5-on-3 Irish power plays. Notre Dame scored the first goal midway through the first period with a cross-crease pass from junior forward Dylan Malmquist to the junior forward Andrew Oglevie that beat redshirt junior goalie Sean Romeo to the far side of the net for his twelfth goal of the season. Ohio State answered back at 6:52 of the second period on the power play with a goal by senior forward Kevin Miller between the legs of Morris. Junior forward John Wiitala and sophomore defenseman Gordi Myer had the assists. Just three minutes later, Ohio State couldn’t get the puck out of its zone. The puck eventually found Notre Dame senior defenseman Jordan Gross in the slot. Gross fired a quick shot past Romeo low to the stick side to give the Irish the lead and the eventual game-winning goal. The Buckeyes pulled the goaltender with just over two minutes to go in the third period with an attempt to tie the game, but Morris stood tall preserving the Big Ten title for the Irish. Ohio State head coach Steve Rohlik made a lineup change to take junior forward Dakota Joshua out, replacing him with sophomore forward Sam McCormick. Joshua had six points in his past five games. It was a coach’s decision, according to the NBCSN broadcast. Shots favored the Buckeyes over the Irish 32-29. Ohio State senior forward Matthew Weis failed to register a point in the contest, snapping his 12-game point streak. Romeo made 27 saves in the losing effort for the Buckeyes. Game Two The second game of the weekend series was a different story, with the Buckeyes scoring five goals and chasing one of the best goaltenders in the nation from his net in a 5-1 win. After strong puck movement in the Irish zone, the Buckeyes opened the scoring when Weis tipped in a shot from the point off the stick of sophomore defenseman Matt Miller past Morris at 9:49 of the first period. On a delayed penalty, Ohio State redshirt senior defenseman Matt Joyaux wired a slapshot from the blueline through a maze of players and into the net for his first goal of the season. Notre Dame answered late in the second period with a rare power-play goal given up by Ohio State off the stick of Oglevie, his second goal of the weekend series to get on the board and cut the Buckeye lead in half.Notre Dame had 23 shots in the second period, pushing its shot lead to 33-25 heading into the third period. The Buckeyes put the game away in the third period with a couple of quick strike goals by Kevin Miller and McCormick 57 seconds apart to push the lead to three goals, ending Morris’ night. Ohio State added an empty net goal to put to the final score to 5-1. Weis’ first period goal gave him 10 goals on the year joining four other Buckeyes in double digit goals; Junior forward Freddy Gerard (10), Joshua (11), sophomore forward Tanner Laczynski (12) and junior forward Mason Jobst (12). Romeo recorded a career-high 39 saves, including 22 in the second period.Morris made 27 saves in the loss, while Notre Dame freshman goaltender Dylan St. Cyr made six saves in relief of Morris.
Ohio State redshirt junior goalie Sean Romeo (30) prepares for a Badger shot in the first period of the game against Wisconsin on Feb. 23 in the Schottenstein Centern. Credit: Jack Westerheide | Photo EditorAfter an expectation-defying season, the Ohio State men’s hockey team that was once predicted to finish in the bottom half of the Big Ten now enters the NCAA tournament on a mission for redemption.The Buckeyes (24-9-5, 14-8-2-1 Big Ten) begin the tournament as a No. 1 seed for the first time in program history a year after being bounced in the first round by eventual runner-up Minnesota Duluth, and take with them an added level of confidence.“Experience is always a good thing, and last year we had nobody with experience,” head coach Steve Rohlik said. “Certainly we’ve got a bunch of guys that were there on the big stage and in a big game, tight game, and I think that’s only a real positive for us.”PrincetonAfter reversing their position from a four seed to a one, Ohio State must start off its tournament against Princeton (19-12-4, 10-10-2 ECAC), a team coming in with seven straight wins and two of the nation’s top four scoring players with junior forwards Max Veronneau and Ryan Kuffner.The duo combined for 46 goals and 107 points while leading the most successful power play in college hockey. “They’ve got a fantastic power play so, again, when a team is playing their best you know you’ve got to be prepared and certainly we have to be prepared for these guys,” Rohlik said.Fortunately for the Buckeyes, avoiding the penalty box and being a man down have been strengths for them all season, ranking first in the nation on the penalty kill while committing the fourth-fewest penalties per game.For Ohio State to make it out of Saturday’s opening matchup, it will need to contain Veronneau and Kuffner, maximizing opportunities against the Tigers’ subpar defense and penalty kill, which both rank in the bottom half of the NCAA.DenverIf Ohio State escapes the clutches of the red-hot Tigers, it will most likely be met by the defending national champions.Denver (22-9-8, 12-6-6-4 NCHC) narrowly missed out on a top seed thanks to the Buckeyes, but looks just as menacing coming out as a two seed.All eyes will be on junior forward Troy Terry, who scored 44 points for the Pioneers a year after being an American shootout hero for the United States at the World Junior Championships. Terry is one of the strongest playmakers in the nation, but sophomore forward Henrik Borgstrom leads the team in goals with 22 and points with 50.Denver is a top 10 team on both sides of the puck and gets in the penalty box even less than the Buckeyes while senior goalie Tanner Jaillet ranks fifth in the nation in goals against average.If Ohio State gets matched up against the Pioneers, it will come down to doing what it has done all year by playing strong hockey on all sides, but even then, it might need some help from the team’s backbone, redshirt junior goalie Sean Romeo.“You’ve got to be playing well, you’ve got to be a little lucky, got to have a hot goaltender, you’ve got to be healthy, I think those are all really keys,” Rohlik said.Penn StateThe other opponent the Buckeyes could match up with in the following round is Penn State, an opponent that has given Ohio State trouble for much of the season.Penn State (18-14-5, 9-10-5-2 Big Ten) is one of four Big Ten teams to make the tournament, a feat sophomore forward Tanner Laczynski said shows the strength of the conference.“That just goes to show the depth of the Big Ten and how far the program has come, and all the programs in the Big Ten,” Laczynski said. “I think that’s big for the Big Ten and big moving forward.”The Nittany Lions defeated Ohio State in three of the four matchups this season, including one shootout win and two wins by over three goals.Junior forward Andrew Sturtz gave the Buckeyes the most problems, scoring in all three victories for Penn State and leading the team with 40 points.Three of the four matchups in the season between these two ended in blowouts, so Romeo will be a key once again to limit Penn State’s offense that shoots more than any team in the NCAA.If Ohio State can get pucks in the net quickly against a weak defensive team and get a lead early, it should have no trouble ending its struggles against the Nittany Lions.Losing senior forward Matthew Weis to injury for the time being will be a major blow to the Buckeyes, given that he is one of the most well-rounded players on the team, but Ohio State may just be deep enough, and strong enough defensively, to make a deep tournament run.