After one of the most astonishing score lines in the history of the World Cup on Tuesday — Germany 7, Brazil 1 — nothing that happens in Sunday’s World Cup final would be a total surprise. But we do have estimates of the most likely final scores for the game.Germany is a 63 percent favorite to defeat Argentina, according to the FiveThirtyEight forecast. Argentina had a slightly higher Soccer Power Index (SPI) rating when the tournament began, but Germany has seen its rating rise, particularly after its thrashing of Brazil, and it now ranks No. 1 by some margin. Betting lines also have Germany favored.The SPI match predictor allows us to predict the number of goals scored and allowed for each club. It calls for 1.7 goals by Germany and 1.2 by Argentina.There are a couple of problems with this — for one thing, a team cannot score seven-tenths of a goal. So the match predictor uses a version of a Poisson distribution, which calculates the probability of the teams finishing with any whole-number score. For example, if Germany scores an average of 1.7 goals, how often does it score exactly two goals or exactly three goals? That’s what a Poisson distribution does.Another issue is that the match predictor is calibrated on the basis of 90-minute matches when knockout-round games can go to extra time. To account for extra-time results, we ran an additional Poisson regression based on the results of extra-time games in major international tournaments since 2005. (In geek speak, we’re nesting a Poisson distribution within another Poisson distribution.) All of that produces the following heat map:Read left to right for Germany’s score and top to bottom for Argentina’s. Boxes in which the score is still tied after extra time represent cases where the game goes to penalty kicks (there is about a 14 percent chance of this happening). The 10 most probable scores are as follows:Germany 2, Argentina 1Germany 1, Argentina 0Argentina 2, Germany 1Germany 2, Argentina 0Argentina 1, Germany 0Germany 3, Argentina 11-1 draw (game goes to penalties)Germany 3, Argentina 2Germany 3, Argentina 0Argentina 2, Germany 0What are the odds of another 7-1 scoreline? The model says there is only about a 0.06 percent probability of such a score favoring Germany (about one chance in 1,600). There’s even less of a chance — more than 10,000-to-1 against — of the same score favoring Argentina.But these figures may underestimate the chance of astonishingly lopsided results. The mathematical basis for the Poisson distribution is the assumption of independent trials. This is a little inexact (it describes a special case of a Poisson distribution called a binomial distribution), but a Poisson distribution is treating a soccer game something like this:Suppose we expect Germany to score 1.7 goals on average in a 90-minute game against Argentina. That translates into about a 2 percent probability (1 chance in 50) of scoring a goal in a given minute of play.So we can run an experiment where we randomly draw ping-pong balls from a set of 90 lottery machines, one representing each minute of the game. In each machine, there are 50 balls, one labeled GOAL! and 49 blanks. The probability of drawing a GOAL! from one machine doesn’t affect what happens with the next one. (This is the assumption of independent trials.) After we’ve drawn balls from all 90 machines, we count the number of GOAL! balls. This represents how often Germany scored in the game.We can repeat the experiment a bunch of times. Most commonly, we’ll wind up with something like one or two GOAL! balls. But other times we’ll have drawn zero or four or six. The relative frequency of these outcomes represents the Poisson distribution for Germany’s score.As strange as this experiment might seem, it isn’t a bad mathematical approximation of a soccer game. And for the most part, Poisson distributions do a good job of modeling real-world soccer scores.But there are some complications. For instance, we may have some estimate of how the absences of Neymar and Thiago Silva might affect Germany’s chances of scoring against Brazil. But there is some uncertainty around that: Maybe Brazil plays more fluidly when it isn’t waiting around for Neymar to do something, or maybe it breaks down. This is equivalent to not knowing exactly how many GOAL! balls and blanks there are in the ping-pong machines. This uncertainty will tend to slightly increase the number of extreme outcomes (Germany scoring zero goals or a lot of goals) that we observe in the real world.Another issue is that the texture of play in soccer depends to some extent on the scoreline. Play is usually tighter and more conservative in a drawn game and then opens up once the tie is broken. As a result, standard Poisson distributions slightly underestimate the chance of draws and of some wild scores, such as 5-2. (The variant of the Poisson distribution that we use is meant to address this problem.)For the most part in sports, these complications are not worth worrying about. There are cases where a Poisson distribution or a normal distribution isn’t perfect — normal distributions seem to slightly underestimate the number of extreme outlier scores in sports — but they usually hold up reasonably well. Nobody gets hurt when you say that Germany has only a 1-in-4,000 chance of winning by six goals when it actually had a 1-in-400 chance.But real-world distributions are often slightly fat-tailed, meaning that extreme outliers happen more often than the normal distribution predicts. And — outside the sports world — using the wrong model can cause real problems, underestimating the chance of an earthquake or a financial crisis.
This year’s NHL trade deadline saw quite a few transactions — 74 veteran players switched teams in the month leading up to (and including) the March 2 moratorium — and some of the moves could shift the league’s balance of power with the playoffs a little more than a month away.In anticipation of Monday’s cutoff, we listed about 35 likely trade candidates and their possession metrics, to get a sense of who the advanced statistics would favor if any of them were dealt. But now that all the deals have been cut, how highly do the numbers regard the big names moved at the deadline?It totally depends on which numbers you look at. Conventional stats — such as goals, assists and plus-minus, as synthesized into point shares above replacement (PSAR) — favor players like newly acquired Detroit winger Erik Cole. Cole bounced back from a pair of down seasons to average a goal every three or so games with a +4 rating (on a Dallas team that’s -11 overall) before being traded. That performance was enough to lead all deadline acquisitions in 2014-15 PSAR. But as we’ve learned, the NHL’s #EnhancedStats movement emphasizes more than traditional counting statistics.Advanced metrics such as Corsi and Fenwick (ahem, “shot attempts” and “unblocked shot attempts”) started a trend in player evaluation of focusing on his ability to improve his team’s puck-possession rate while on the ice. If possession is a reliable path to team success, the reasoning goes, you want to stock your roster with players most associated with strong team possession rates when they’re in the game.Now, Stephen Burtch’s Delta Corsi (dCorsi) and Domenic Galamini’s Usage-Adjusted Corsi have pushed attempts to isolate a skater’s effect on his team’s possession rate even further. The relatively new twist provided by those stats? Attempting to account for player-usage factors — such as position played, teammate and opponent quality, zone starts and even faceoff winning percentages in dCorsi’s case — on a player’s possession rate in addition to looking at on-ice versus off-ice differences.In the past, you’d have to eyeball a player’s workload and usage as a means of context for, say, his relative Corsi. But these new stats attempt to bake those contextual factors into a single number by comparing a player’s actual possession rate to what we’d expect of an average NHL player at his position if placed in the same situations.1This is similar in theory to the way researchers have sometimes attempted to measure individual fielding in baseball, under which a defender’s actual plays made in the field are compared with expected play counts based on balls in play sent in his direction.You might think there’d be a decent amount of crossover between conventional numbers and these new possession-based advanced stats, but the correlation is practically nonexistent. Rescaling PSAR against an average baseline to make an apples-to-apples comparison, I found essentially no relationship with Burtch’s dCorsi Impact (which gives players more credit for maintaining strong possession rates relative to average in greater amounts of ice time) this season:Take Cole again. Despite his solid counting stats and a very good point share tally, Dallas’s possession rate when Cole was on the ice was actually lower than what would be expected from an average player in the same situations with the same teammates and opponents. Or take FiveThirtyEight favorite Jaromir Jagr, whose relatively down conventional stats belie a player still capable of driving play with the proverbial skills that don’t show up in the box score.They’re not alone among the bigger-name deadline acquisitions. Much was made when the Arizona Coyotes shipped away center Antoine Vermette and defenseman Keith Yandle. Both players were solid PSAR contributors for Arizona this season but also ranked among the least valuable dCorsi players at their respective positions.Meanwhile, Zbynek Michalek, another former Coyote, boasted extremely unimpressive counting numbers (8 points and a -6 rating in 53 games) even by the standards of his position but ranks as one of the best defensemen in hockey according to dCorsi Impact.In case it wasn’t clear by now, all this goes to show that it’s nearly impossible to guess whether a player is a possession star or scrub based on his conventional numbers. As is the case with most of these new-school-versus-old-school metric battles to recently crop up across almost all sports, a player’s true value probably lies somewhere in between. But in hockey, that fact just underscores how little we still know about who’s helping and hurting their teams.
In the most surreal bit of sports news to hit Tuesday’s wires, Geno Smith, the incumbent starting quarterback for the New York Jets, is going to miss six to 10 weeks with an injury. And it wasn’t just any kind of injury; it was the kind you get when a teammate punches you and breaks your jaw.According to ESPN’s Adam Schefter, now-cut linebacker IK Enemkpali slugged Smith because he failed to reimburse Enemkpali for a $600 plane ticket. Jets fans — at least, Jets fans in the FiveThirtyEight office — rejoiced. But coming into the preseason, this Jets team showed at least a vague sense of promise. Are the Jets really better off without Smith? Last year, New York went 4-12 (3-10 under Smith) with one of the NFL’s worst passing offenses, and Smith was pretty bad for the second straight season. In fact, his career has gotten off to one of the worst starts of any QB since the 1970 AFL-NFL merger. Although Hall of Famers Troy Aikman and Terry Bradshaw kicked off their careers with equally bad passing efficiency numbers, it’s far more likely that Smith is a talent on par with, say, Kyle Boller than that he’s harboring hidden star potential.And while Smith will be 25 this season, an age at which QBs still show a decent amount of improvement, the typical aging curve wouldn’t even carry Smith to league-average status at his peak.Meanwhile, the Jets’ backup quarterback isn’t a bad one. Longtime Buffalo Bills starter Ryan Fitzpatrick occupied the seat behind Smith and — statistically speaking — he’s probably a better QB than the man slated for the huddle, at least in the short term. Over the past two seasons (when he was with the Houston Texans and the Tennessee Titans), Fitzpatrick’s Total QBR1ESPN’s play-by-play-based measurement of quarterbacking performance, which grades on a 0-100 scale. of 54.1 was better than Smith’s 41.2 mark during that span, and Fitzpatrick’s cumulative ProFootballFocus grade of +11.3 vastly outpaced Smith’s total of -37.3. By the numbers, the Jets are better off with the glass-jawed Smith out of the game, holding a clipboard on the sidelines.Of course, these kinds of assessments always come with the caveat that individual football statistics are obscured by countless interactions, coaching decisions and other contextual factors. And the Jets did add a good receiver over the offseason, Brandon Marshall, so it’s possible Smith would have improved significantly in 2015 had he not run afoul of Enemkpali’s fist. But Jets fans shouldn’t worry too much about what might have been: The team’s offense will probably be better with Fitzpatrick under center than it would have been with Smith.
The NFL’s conference championship weekend is finally here. In the video above, FiveThirtyEight’s Neil Paine breaks down the two matchups. Share on Facebook
If you’ve been paying attention to the 2017-18 NHL season, you may have noticed something: There is no shortage of goals. We’re more than a third of the way through the season, and scores are, on average, the highest they’ve been since 2005-06. Through Sunday, there have already been 31 games settled in regulation in which the teams combined for double-digit goals. A year ago there were only 45 such games across the whole season. We’re still a long way away from the 1980s golden era of goals, but for a league that’s been repeatedly criticized for being too low-scoring, this sudden glut can only be read as a positive development.It’s difficult to point to just one cause for the scoring uptick. The league instituted a number of rule changes at the beginning of the season,1The NHL did something similar after the lockout of 2004-05, introducing a sweeping array of rule changes with the express purpose of increasing goal scoring. and those changes have contributed to an increase in power play opportunities — teams are averaging 3.3 power play opportunities per game so far this season, the highest mark in five years.2Those changes also include a crackdown on slashing and a conservative interpretation of what a center can and cannot do at the faceoff dot: If a center does not stand squarely facing his opponent’s side of the rink, he is subject to being thrown out of the dot. If that happens consecutively to centers from the same team, that team will be subject to a bench minor. The league wants to see less stick work and less cheating on the faceoff dots. And it’s evident the league wasn’t kidding about cracking down on slashing — through Sunday, officials had doled out 623 penalties for slashing. That number was just 791 for all of last season. Slashing0.641.22+0.58 Hooking1.030.97-0.07 Holding0.640.67+0.03 The box is getting crowdedMinor penalties per NHL game over the past two seasons Roughing0.840.67-0.18 More power play opportunities translate to more odd man advantages, which translate to more shots on net per game: Teams are surrendering more shots per game (31.6) than they have in the past three decades. The math is simple: more shots on goal equals more pucks finding the twine.And even though goals against averages are up across the league — the current mark of 2.76 is the highest it’s been in a decade — the goaltenders cannot be blamed for the league’s recent scoring outburst. Among qualifying goalies,3We looked at goalies who’ve played at least 200 minutes. the league average save percentage (92.18) is slightly higher than the league average expected save percentage (92.13), which is the save percentage an average goalie should post given the quality of shots faced.This suggests that goalies are actually outperforming expectations. During the 2016-17 season, goalies stopped fewer pucks than the data suggested they should have stopped (an actual save percentage of 91.94 versus an expected save percentage of 92.17), and yet goals per game totals remained roughly in line with numbers from the preceding nine seasons.Goalies in 2017-18 are also outperforming their peers from the previous year on high danger shots (unblocked shots with an expected scoring percentage of 9 or greater); this year’s cohort is stopping 79.4 percent of shots considered dangerous, while last year’s stopped just 78.5 percent of those shots.Even though teams are converting their power plays with effectively the same efficiency they did in 2016-17, they’re getting more of those man advantage looks, and likely the rule changes — and not poor goalie play4Goalies are always getting blamed; we need to stop blaming those poor goalies. — offer the truest explanation for the higher-scoring brand of hockey being played in the NHL at the moment.And let’s not forget the cadre of young offensive talent that has flooded the NHL in the past several seasons: Last year, six of the top dozen goal scorers were 25 years old or younger. And the top point getter (Connor McDavid) was just 20. All of these very young men are already sharing the leaderboards with future Hall of Famers like Sidney Crosby and Alex Ovechkin, who are still both in the primes of their careers. This overlap of generational talent could be contributing to all the scoring, too.To be sure, no one in the league office of the NHL is losing sleep over this. More scoring is good news for a league that has been plagued by a spate of absurd calls from pundits to not only tweak the rules of the game, but to change the shape of it too. Reprimanding centers for lining up at the dots incorrectly? Fine, we can all live with that. But making the nets bigger? That’s downright sacrilege. Thanks to the relatively high-scoring climate of the present-day NHL, maybe we can put that tired argument to bed once and for all. Interference0.640.71+0.08 Goalie interference0.100.09-0.01 High stick0.690.58-0.10 Tripping1.081.20+0.12 Holding stick0.110.07-0.03 Penalty2016-172017-18Difference All minors6.657.09+0.44 Cross checking0.300.31+0.01 Through Dec. 17. Differences may not add up because of rounding.Source: ESPN
Past 572 at bats, .247 becomes possible at three of every four numbers. So last year’s quest got quite a bit easier on Sept. 29 when Davis hit at-bat number 572, a career high. The odds of Davis ending the past four seasons with a .247 batting average even with the perfect number of hits are still just 9.4 percent.Put another way: Davis finished 2018 with 576 at- bats. If he had gone to the plate just one more time and gotten a hit, he would have finished with a .248 average. If he had made an out, he would have been at .246.1A walk would have left it the same, of course. Davis was pulled in the sixth inning of the season’s final game, by the way.The chase for .247 isn’t impossible, but it is mathematically very unlikely. Of course, it was also unlikely the past four seasons. The powers of rounding and opposing pitchers haven’t stopped Davis before. We believe in you, Khris.Check out our latest MLB predictions. The most amazing active streak in baseball might be the most unlikely streak in history.I’m talking, of course, about Khris Davis’s batting average. Davis has hit exactly (or, more precisely, rounded to) .247 for not one, not two, not even three but four straight seasons, from 2015 through 2018.This year appears to be different. Davis is currently hitting .230, and while there’s still a long way to go this season, .230 is not close to .247. Baseball appears to be broken.Assuming he stays healthy the rest of the year and plays in roughly the same amount that he did in 2018, Davis needs to hit .282 for the rest of the year to continue the streak. That’s certainly not impossible.Davis also should be hitting closer to .247 based on some of his advanced metrics: He has a higher average than he did last year on balls in play (.274 to .261) and a lower strikeout rate (26.3 percent to 26.8 percent). With those components improving, we’d expect Davis to have a better batting average. Statcast metrics tell a similar story. Davis is seeing roughly the same pitches to previous seasons and swinging and making contact about as often. The difference isn’t seeing better pitchers.One clear difference with Davis — in addition to his inexplicably non-.247 average — seems to be his power. Davis has hit just 17 home runs in 2019. He finished last year with 48, and at this point in the year he had hit 32.Likewise, his hard-hit rate and average launch angle have plummeted. The San Francisco Chronicle reported that a pair of injuries may be limiting him, and Davis said he’s been choking up, possibly leading to weaker hits.Some of the difference, as is often the case in baseball, is luck. Davis has made 24 outs this year on balls in play with an expected hit probability (based on launch angle and exit velocity) of over 50 percent. This particular lineout had a .797 expected batting average, or a nearly 80 percent chance of landing for a hit:Video Playerhttps://fivethirtyeight.com/wp-content/uploads/2019/08/9aa1f874-c4f7-4697-a17a-223828fbd499.mp400:0000:0000:17Use Up/Down Arrow keys to increase or decrease volume.Overall, according to Statcast, Davis has an expected batting average of .242 based on all his batted balls. With slightly better luck, Davis could be right where he’s been the last four years.It’s worth remembering how fickle batting average can be. The difference between Davis’s batting average from this year and last year is just six missing hits, which says a lot more about batting average than it does Khris Davis. It also suggests that the decline in homers alone might be keeping him out of .247 range.To reach the fabled average, Davis needs a hot finish to the season — and, fortunately, he’s had even better hot streaks before in his career. Last year, from June 29 to Aug. 23, he hit .316 in 177 at-bats with 19 home runs over 46 games. Davis will have about as many at-bats for the rest of the year, and he doesn’t need as many hits. If the power comes back, it’s easy to imagine a similar stretch.Besides opposing pitchers, another enemy threatens Davis’s batting average streak: rounding.Let’s say Davis finishes the season with 520 at-bats, a reasonable number based on his playing time. If he has 128 hits in those at-bats, his average rounds to .246. With one more hit, though, it would increase to .248. There’s a decent chance that Davis has no mathematical chance of repeating his .247 average.The odds of having a number of at-bats for which a .247 average is impossible are close to 50-50. If Davis finishes with a number of at-bats between 500 and 571 that is two or three more than a multiple of four, he can hit .247. Otherwise, he cannot.
OSU redshirt sophomore QB J.T. Barrett (16) stiff arms Penn State redshirt sophomore defensive end Garrett Sickels (90) during a game at Ohio Stadium on Oct. 17. OSU won, 38-10. Credit: Samantha Hollingshead / Photo EditorOn Saturday morning, the Big Ten East’s four heavyweights (Michigan, Michigan State, Ohio State and Penn State) were all undefeated in conference play and a combined 22-2 on the season (those two losses were against currently undefeated teams in Utah and Temple). After a stunning victory by Michigan State and an excellent performance by OSU, the true top dogs of the division have become just a little more clear.With the season already halfway done, OSU coach Urban Meyer still seems no closer to deciding on which quarterback will take the reins to the offense than he did during the entire offseason. After OSU’s most dominant performance of the season, however, it doesn’t seem to matter. At least not yet.Despite once again falling behind early and failing to score in the first quarter, the OSU offense stepped on the gas in the second and put up 21 unanswered points against a stout Penn State defense en route to a 38-10 blowout win.Ever since Meyer began to phase J.T. Barrett into the game more last week, the redshirt sophomore quarterback has been electric, leading the Buckeye offense to 10 touchdowns and a field goal in 11 red zone appearances. Barrett finished the game completing all four of his passes for 30 yards and two touchdowns to go along with 102 yards and two more touchdowns on 11 carries on the ground.Redshirt junior Cardale Jones ended the game 9-of-15 passing for 84 yards, his lowest yardage total for the season. Meanwhile, junior running back Ezekiel Elliott, a stalwart for the Buckeye offense, pounded away 153 yards and a touchdown on 27 carries. In total, OSU managed to gain 429 yards and 25 first downs against one of the top defenses in the nation.Penn State junior quarterback Christian Hackenberg, claimed by some to be the highest-rated quarterback for next year’s NFL draft, was ineffective all night long, finishing the night 7-of-13 passing for 120 yards. However, true freshman running back Saquon Barkley looked impressive for the Nittany Lions as he gashed the Buckeye defense for 194 yards on 26 carries after missing the previous two games for Penn State.Penn State will host a struggling Maryland team next week as it fights to become bowl eligible, while OSU will travel to Rutgers in search of its nation-leading 21st straight victory. By the numbers:4: OSU’s current win streak against Penn State, its longest in the history of the programs.17-13: The all-time record between Penn State and OSU, favoring the Buckeyes.1: The amount of losses OSU has suffered against Big Ten competition at night in Ohio Stadium. The team that beat them? Penn State in 2008 by a score of 13-6.23: The number of consecutive games in which redshirt junior wide receiver Michael Thomas has caught a pass for the Buckeyes. He currently leads the team with 433 yards and 30 receptions.108,423: The number of fans crammed inside the ‘Shoe last night, the second-largest crowd in history to watch the Buckeyes play.9: After last night, Elliott became the ninth running back in OSU history to rush for 3,000-plus career yards. He currently has 3,028 yards to his name.38: Under current coach James Franklin, Penn State prides itself on having a great defense. The 38 points put up by the Buckeyes were the most given up by Penn State in a game since 2013, when the Nittany Lions also played in Columbus and got embarrassed 63-14. 3: Since 2010, Penn State has allowed its opponents to rush for more than 300 yards in a game only three times. All three times, it was against the Buckeyes inside Ohio Stadium.
Sophomore forward Keita Bates-Diop talks to the media on Feb. 23 for the first time since his surgery on his left shin. Credit: Jacob Myers | Assistant Sports EditorNot much has gone right for the Ohio State men’s basketball team this year, making it all the more difficult for sophomore forward Keita Bates-Diop as he goes through rehabilitation after surgery on his left shin.The Buckeyes lost arguably their most dynamic player following a 76-75 loss to Purdue on Jan. 5. Bates-Diop sat out that game and coach Thad Matta informed the media in the postgame press conference that the forward would redshirt and be out for the year. Since that game, Bates-Diop has had surgery and his recovery is ahead of schedule.But the injury could have been devastating.“It was getting close to what could’ve been a compound fracture, which — Kevin Ware, Paul George — that’s what would’ve happened,” Bates-Diop said. “Don’t know when it would’ve happened, but if I would’ve kept playing, it was a possibility. It was already decided I wasn’t playing, but when they said that, there was no chance I was coming back.”The 6-foot-7 forward from Normal, Illinois, had been dealing with the injury since the summer when another player made hard contact with his shin. He came back and still wasn’t 100 percent, so the team mandated that he receive X-rays. “(The coaches) could tell something was wrong,” Bates-Diop said. “I wasn’t playing like myself. I was playing at a high level in the summer, until I did something wrong.” Bates-Diop then suffered a right ankle injury against Providence on Nov. 11. He missed five games with that injury, but it wasn’t until December that his shin began bothering him again. Bates-Diop recalled stopping abruptly versus Youngstown State on Dec. 20 when he felt a pain shoot through his leg. From then on, his injury didn’t progress any and he was shut down for the season.Through the season Bates-Diop said he was near full health. He admitted on Thursday that wasn’t the case.“I told you guys that I was close to being 100 percent. I was not,” he said. “I lied. I was never 100 percent. I was never close.”Bates-Diop said that he had a rod placed in his left leg from just below the kneecap down to just above the ankle. He has two screws in his leg, one in each spot to anchor the rod. He said that the one near his ankle will likely be taken out. He was supposed to be on crutches for two to three weeks but was only on them for one week.As far as conditioning and lifting is concerned, Bates-Diop is working on upper-body strength, so by the time summer rolls around he can jump into skill drills.On top of all that, two weeks ago, Bates-Diop’s brother collapsed during practice and was transported to the hospital, causing the Buckeye to head home. His brother was discharged from the hospital days later and Bates-Diop said his brother returned to school Thursday for the first time.While that was going on, OSU has fallen to 15-13 and 5-10 in the Big Ten before it plays No. 16 Wisconsin on Thursday night. Bates-Diop said one of the most difficult aspects of his injury is knowing he can’t contribute to a team who’s struggling mightily. “It’s been very hard,” he said. “Just dealing with everything I’ve been personally through and then the team. It’s been a rough few months … since New Years.”Bates-Diop was averaging 9.7 points and 5.2 rebounds before the injury. He said that he has taken a new role on the sidelines and has a new perspective outside the game that other players don’t have — something he’s hoping to carry over to next year.“I think mental preparation toward the game. I was playing at a high level. I can get that back,” he said. “But looking from the outside in, now I can change my mentality from the game. All that stuff I can teach these guys, especially the younger guys on what to do and what not to do.”
The Big Ten Conference released the 2017-18 men’s basketball conference schedule, giving Ohio State first-year coach Chris Holtmann his first look at a packed calendar, which features two games in December and home-and-homes with Michigan and Indiana.The Big Ten was forced to schedule two conference games in early December due to the conference tournament being played at Madison Square Garden in New York, the week before the NCAA Tournament is unveiled in March. Traditionally, the Big Ten tournament has been the final conference tournament to end before the NCAA selection show.Ohio State will open Big Ten play with a trip to Madison, Wisconsin, on Dec. 2 for its only date with the Badgers, which follows the Phil Knight Invitational in Oregon and the Big Ten/ACC Challenge game against Clemson. Then the Buckeyes play at home two days later versus Michigan.Holtmann and his team will play one game at home against Michigan State (Jan. 7), Maryland (Jan. 11), Nebraska (Jan. 22) and Illinois (Feb. 4), and they will play one game on the road against Wisconsin, Northwestern (Jan. 17), Minnesota (Jan. 20) and Purdue (Feb. 7).Ohio State’s conference opponents it will face twice are Michigan, Indiana, Rutgers, Penn State and Iowa.The Buckeyes have three consecutive road games from Jan. 14 to Jan. 20 followed by a four-game home stand from Jan. 22 to Feb. 4. Ohio State will end the season at Indiana, Holtmann’s third away game in four games. Sun. Nov. 5vs. Wooster (exhibition)Columbus Sat. Dec. 16vs. Appalachian StateColumbus Sun. Nov. 19vs. NortheasternColumbus Sat. Jan. 20vs. Minnesota (Super Saturday)New York (Madison Square Garden) Thu. Jan. 25vs. Penn StateColumbus Thu. Nov. 23vs. Gonzaga (PK 80)Portland, Oregon Sun. Jan. 7vs. Michigan StateColumbus Mon. Jan. 22vs. NebraskaColumbus Sun. Feb. 18at MichiganAnn Arbor, Michigan Sat. Feb. 10vs. IowaColumbus Mon. Dec. 4vs. MichiganColumbus Tue. Feb. 20vs. RutgersColumbus Fri. Nov. 24Florida/Stanford (PK 80)Portland, Oregon Tue. Jan. 30vs. IndianaColumbus Sat. Dec. 23vs. North Carolina (CBSSports Classic)New Orleans Thu. Jan. 11vs. MarylandColumbus Wed. Feb. 7at PurdueWest Lafayette, Indiana Tue. Dec. 19vs. The CitadelColumbus Sat. Dec. 30vs. Miami (OH)Columbus Sun. Jan. 14at RutgersPiscataway, New Jersey Fri. Feb. 23at IndianaBloomington, Indiana Sun. Nov. 12vs. Radford Columbus Sun. Nov. 26TBA (PK 80)Portland, Oregon Sat. Dec. 2at WisconsinMadison, Wisconsin Sat. Dec. 9vs. William & MaryColumbus Sun. Feb. 4vs. IllinoisColumbus Thu. Jan. 4at IowaIowa City, Iowa Fri. Nov. 10vs. Robert MorrisColumbus Wed. Jan. 17at NorthwesternRosemont, Illinois (Allstate Arena) Thu. Feb. 15at Penn StateUniversity Park, Pennsylvania Wed. Nov. 29vs. Clemson (Big Ten/ACC Challenge)Columbus Thu. Nov. 16vs. Texas Southern Columbus
Members of the men’s hockey team huddle up prior to the start of game one during Ohio State’s hockey game vs. Michigan State on March 1. Ohio State won 5-1. Credit: Nick Hudak | For The Lantern The Ohio State Men’s Ice Hockey team took on Michigan State on March 1 and March 2 at the Schottenstein Center in Columbus. Ohio State won game one 5-1 but lost game two 3-2. Photos by Nick Hudak and Casey Cascaldo.