Month: September 2019 Page 1 of 20

A Closer Look At The Biggest Jumps At The X Games

With all the hifalutin analytical tools available to sports like baseball, basketball and tennis, younger sports BMX and skateboarding probably aren’t the most statistically sophisticated events going. Fair enough — but the foundation is being built for them faster than for sports that haven’t yet undergone a statistical revolution, like boxing or hockey. At the summer X Games last weekend, that came in the form of a little motion-tracking gadget called Curie. Big Air skater Trey Wood, with the Curie on his helmet. Brent Rose Curie is a pod that captures jump height, jump distance, in-air rotation and impact upon landing. The contraption, made by Intel, is just 1.5 inches by 1 inch by 1 inch and weighs three-eighths of an ounce. This year, it was attached to bikes for the BMX Big Air and BMX Dirt and to riders’ helmets for the Skateboard Big Air event at the games, which took place in Austin, Texas (although because of bad weather, it only saw action in BMX Dirt). Despite its diminutive size, the puck packs in an impressive suite of sensors, including a 9-axis accelerometer, gyroscope, electronic compass, barometer and GPS — plus a built-in 900-MHz radio to beam the stats to ESPN, which owns and broadcasts the games.1ESPN also owns FiveThirtyEight. The Curie is not available to consumers — for now, it’s mainly a promotional tool for Intel among pro riders — but its closest competitor would be the Trace, which provides a similar function for surfing and snowboarding, though with far less granularity.Here’s a clip of the Curie in action in Austin:The Curie made its debut at the winter X Games in Aspen, Colorado, in January. That first version of the pod was three times larger and heavier than its current iteration. In Colorado, the pod was mounted directly on to the decks of snowboards in the men’s Snowboard Slopestyle and men’s Snowboard Big Air contests. On his gold medal Slopestyle run, Canadian Mark McMorris hit the final jump at 43 mph, rotated 1,440 degrees on his backside triple cork, and landed with an impact force of 11 times the Earth’s gravity (11.1 Gs). He traveled 83.5 feet.That setup had to change for the summer games. Because a snowboard is attached directly to a rider’s feet through boots and bindings, it was easy enough to mount the pod on the board itself. But skateboarders are attached to their boards only via gravity and friction, so unobtrusively mounting one was more complicated. If a skater was rotating in mid-air and did a kickflip at the same time, the pod would report the in-air rotation of the board, but not the skater. Ultimately, Intel and ESPN had to decide which data set would be more compelling. It chose to stick Curie on the rider’s helmet, not only because it would likely deliver better data, but also because if a rider’s foot happened to catch on the Curie while coming off the 27-foot quarter pipe, the consequences could literally be deadly. Not much chance of riders opting in for that sort of risk. I asked Intel engineer Tyler Fetters if this could be bad data. He insisted that it wasn’t. “That number is the spike at the point of contact,” Fetters said. “That moment is so brief that it goes by in an instant. If they had to sustain it for even a half-second, they would probably be crushed or maybe even pass out.”I asked Wade what a 20-G landing felt like. “It sucks,” he said, wincing. “Every time, my ankles feel like they’re falling apart.”X Games judges haven’t yet integrated Curie data into their scoring. According to Fetters, the technology is just too new, and they want to make sure that it’s accurate close to 100 percent of the time before they consider it. However, he said that athletes and coaches are already asking Intel for a more mobile system (in which Curie could send data to a phone instead of the larger suite of computers and radios that is currently used) that could be deployed at parks and ramps anywhere and could help them get ready for the next big event. While the technology is so new that it has only been deployed at the X Games (and in private testing), there’s potential for it to be applied more widely. I talked to BMX Big Air rider Morgan Wade to see if he would be open to using it in his training.Wade said that he primarily rides by feel but that there are instances in which more information would be a big help in training. “Sometimes the little nuances are so small that you don’t pick up on exactly what’s wrong, but you know that something isn’t right, and that’s where this could be really beneficial,” he said.Say you’re a BMX rider who is going over a 50-foot gap jump. You’re doing a huge 360 while spinning your handlebars clockwise. The jump is under-rotated, and you go down hard upon landing. What happened? Although Curie only displays certain metrics at the end of the run, it’s recording the entire thing. It can tell you that your rate of spin was 180 degrees per second when you first left the ramp but that it slowed to only 90 degrees per second by the time you landed. Was it just the friction of the wind that did that?The data looks similar to an EKG readout, displaying the peaks and valleys over time for each metric. So you can line up that graph against the video footage, and look! The rotational speed decreased dramatically at the moment you spun the handlebars. You might think that next time you should try spinning it the other way or launch with more initial rotation to compensate.One of the stats the Curie displays has baffled me for the past two X Games: the G-forces upon landing. I’ve seen numbers that are higher than 20 Gs, and that just doesn’t compute. If a rider weighed 175 pounds and landed with 20 Gs of force, he would effectively weigh 3,500 pounds (roughly 1,590 kilograms). There’s no way our musculoskeletal system could withstand that. Brent Rose read more

Former Lance Armstrong Teammate David George Tests Positive

David George, a South African cyclist and former teammate of Lance Armstrong, admitted on Tuesday to using the blood-boosting drug Erythropoietin (EPO) after failing a doping test on Aug. 29.”His biological passport indicated suspicious activity and that triggered a targeted test for EPO,” SAIDS chief executive Khalid Galant said in announcing the positive test Tuesday. ”A subsequent urine test came back positive for the banned EPO drug.”George is now facing a two-year ban from Cycling of South Africa, but is provisionally suspended until a final decision is reached.He cycled with U.S. Postal Service team in 1999 and 2000, alongside recently banned Armstrong. George said he realizes that he will face a ban as well and is prepared for what the CSA decides.”I know the result will ultimately be the same. This decision will be communicated to Cycling South Africa (CSA) and Drug-Free Sport shortly and according to protocol,” George said in a statement: ”I fully understand the consequences of my admission and will bear the results of this.”In October, Armstrong was stripped of his seven Tour de France titles and banned for life by the International Cycling Union for his use of steroids, EPO and blood transfusions. Multiple teammates testified against Armstrong in the report issued by the U.S. Anti-doping Agency.While George waits to hear from the CSA, he has already started to lose sponsors despite apologizing. Nedbank has withdrawn its sponsorship from his current 360Life team.”Nedbank has a zero tolerance towards the use of any banned substances or performance enhancing drugs and does not condone or support such use in any sport,” the South African bank said.This is one of George’s lowest moments as a cyclist after having a successful career. He won the silver medal in the road race at 2006 Commonwealth Games in Melbourne and bronze in the time trials in the Kuala Lumpur Games in 1998. George also represented South Africa at the 1996 and 200 Olympics.”Cycling, as you know, has been a confusing space, and although it has given me incredible moments it has also given me experiences that no person or young athlete should have to go through,” George said.Now he must figure out how to get over this mountain in his life. read more

The Cleveland Browns Made History — By Bungling A FourthAnd9 Play

Play TYPENO. OF plays4th down conversion % Screen passes aren’t all that successful on fourth downFourth-down conversion rates by selected play type, 2009-19 regular seasons Zone read4168.3%– Pitch7462.2– Draw1957.9– Screen3735.1– Play-action26357.8– History was made on Sunday Night Football, but likely not the kind Cleveland Browns fans were hoping for. With 9:45 left in the fourth quarter and trailing 17-13, the Browns advanced to the Rams’ 41-yard line. A first-down run by Nick Chubb and two subsequent passes by Baker Mayfield netted Cleveland a single yard, and head coach Freddie Kitchens was faced with a difficult fourth-and-9 decision. In response, he dialed up a play call that is unprecedented in recorded NFL history: a draw.Video Playerhttps://fivethirtyeight.com/wp-content/uploads/2019/09/cle_4thdown.mp400:0000:0000:07Use Up/Down Arrow keys to increase or decrease volume.A draw play is designed to make the defense believe that a pass is coming, but instead results in a delayed handoff to the running back. The version the Browns ran gained just 2 yards, so Cleveland turned the ball over on downs. To be fair to Kitchens, it was a bold call in adverse circumstances. The analytically inclined are sometimes guilty of screaming at the TV for teams to be aggressive on fourth down and then — if the play is unsuccessful — immediately turning around and loudly complaining about the play call. But even given the results-oriented bias of the NFL fanbase, a draw play on fourth down and long is still a very rare and deeply contrarian move.Since 2006 — the first year ESPN began tracking play types — teams have dialed up a draw just 26 times on fourth down, including the Browns’ failed attempt. In that same period, no team outside of Cleveland has called a draw on fourth down with more than 7 yards to go. Among this small sample, just one draw has successfully converted to a new set of downs with more than 3 yards to go — and the quarterback responsible was noted gridiron sorcerer Ryan Fitzpatrick back when he was starting for the Buffalo Bills in 2011.Still, faulting Kitchens for calling a draw just because few have been called historically from that distance might be unfair. Draws work more often than not when they are called on fourth and short, for instance.1Three yards to go or less. And the conversion rate on a slightly more recent sample of plays — there have been 16 since 2009, the year our dataset begins — is a robust 62.5 percent, so the play can work in the right situations. Meanwhile, the success rate for all fourth-down plays with exactly 9 yards to go — the same distance Kitchens was faced with — is an anemic 35.4 percent. It’s hard to blame a specific play type when the odds against success are stacked so high.This made us wonder if there were play types that have historically been successful in fourth-and-long situations. Historical charting is somewhat limited in the NFL, but we found five other play types we could compare to the draw across the period spanning from 2009 to 2019. Sample sizes are notoriously small for fourth- and- long plays due to the NFL’s aversion to aggressive play calling, but among the play types for which we have data, play-action is the most common.There simply isn’t a lot of evidence to guide decision making in these situations. But if we look at the relative frequency of each type of play call and break each out by success or failure, there is a play type that historically has seen reasonable success on fourth and 5 or more: the screen pass.Screen passes are short passes to a receiver that start off looking like deeper passes or runs. Linemen — usually the center and two guards — begin the play blocking as normal but will release their defenders in an attempt to get them to overcommit to rushing the quarterback. A well-executed screen usually requires the QB to wait until the opposing linemen are almost on him before he tosses the ball to the receiver. Ideally, the scrum of suddenly free offensive linemen then begin to rumble downfield in search of a smaller linebacker or defensive back to block into the turf.Acknowledging the fact that we have limited charting of play types, the screen is the only play type that has been successful the majority of the time on fourth down and between 5 and 9 yards to go. The sample size is admittedly tiny — just 10 plays — so any notion of statistical significance goes right out the window, but teams were able to successfully convert a first down on six of the 10 attempts, good for 20 percentage points over league average for the given down and distance.Perhaps the deception afforded by convincing the defense that a long pass is coming improves the play’s outcome. It’s interesting to note that of the play types under consideration, the screen is the worst performing on fourth and short — and on fourth-down plays overall. Scramble17353.8– Source: ESPN Stats & Information Group For screens to succeed, the offense needs defenders to respect the pass and try to sack the QB so space can develop for the receiver to operate. On fourth and short, considerations of containing the pass likely take a back seat to stopping the run. The numbers seem to bear this out. When all distances on fourth down are considered, the screen is the worst-performing play on average.Kitchens and the Browns might have given themselves a better shot at extending their drive had they chosen another play type on Sunday. A screen might have led to a better pay-off for their aggressive play call. Then again, given the dearth of data we have to work with, perhaps it’s just noise. Check out our latest NFL predictions. read more

The Hidden Value of the NBA Steal

Scoring in professional basketball is one of the most beautiful things in sports. With only moments to set up his shot, a player tosses a ball into a soaring arc, and it drops through a hoop only slightly larger than the ball. That or he flies to the hoop and deposits the ball directly.It’s no wonder, then, that individual players’ scoring abilities get the most attention. But basketball is a complex and dynamic sport, and this skill is only one of many that determine what kind of impact a particular player has on the bottom line.In fact, if you had to pick one statistic from the common box score to tell you as much as possible about whether a player helps or hurts his team, it isn’t how many points he scores. Nor how many rebounds he grabs. Nor how many assists he dishes out.It’s how many steals he gets.This phenomenon — that steals is one of the most informative stats in basketball — has important implications for how we think about sports data. But it can also help us investigate real-life basketball mysteries, such as “What the heck is going on in Minnesota?”Consider the curious case of Ricky Rubio. A professional basketball player since the age of 14, he won a silver medal in the 2008 Beijing Olympics (leading a strong Spanish team in assists, steals and even defensive rebounds during the knockout rounds). The Minnesota Timberwolves drafted him in 2009 with the fifth overall pick (age: 18), but he initially stayed in Spain, not making his NBA debut until 2011.During the two years Rubio spent at FC Barcelona, his eventual Minnesota teammate Kevin Love ascended into the ranks of the NBA’s statistical elite. This left many to expect (or hope) that adding Rubio would finally make the Timberwolves a contender. But in his first two seasons, the Timberwolves still haven’t made the playoffs. Going into the 2013-14 season, ESPN’s TrueHoop Network ranked Rubio as the 49th best player in the league (only slightly ahead of teammate Nikola Pekovic). He has struggled with injuries and is considered a terrible, “makes Rajon Rondo look like Reggie Miller”-type shooter.1So far, Rubio has put up the worst effective field goal percentage among regular NBA starters every year of his career.Since entering the NBA, Rubio has been dominant in two major statistical categories: not scoring and steals. Of all players averaging 30-plus minutes, Rubio’s 10 points per game is the third-fewest overall, and the worst of all guards by more than a point.2The second-lowest-scoring guard is Jose Calderon with 11.2 PPG.His 2.4 steals per game, on the other hand, is the second most. It’s only .1 steals behind five-time NBA steals champion Chris Paul (and Rubio edges Paul in steals per minute and steal percentage).What do you do when you have highly divergent indicators such as these? NBA stat geeks have been trying to mash up box score stats for decades. The most famous attempt is John Hollinger’s player efficiency rating, which ostensibly includes steals in its calculation but values them about as much as two-point baskets.3In PER, steals are each worth the value of one possession. A two-point basket is (roughly) worth two points minus the value of one possession. Because a possession is worth about one point, these are both worth about +1 point in Hollinger’s equation. In other words, steals have only a small effect on a player’s PER. Despite his stealing prowess, Rubio has a career PER of 15.6, ranking 82nd in the league for the period. Meanwhile, Love has a PER of 25.7 (fourth in the league) over that same time.Hollinger weights each stat in his formula based on his informed estimation of its intrinsic value. Although this is intuitively neat, empiricists like to test these sorts of things. One way to do it is to compare how teams have performed with and without individual players, using the results to examine what kinds of player statistics most accurately predict the differences.4I used this technique quite a bit throughout my treatise on Dennis Rodman, though it is actually better suited to broader analysis such as this. For this article, I’m using team game “with and without you” (WOWY) comparisons from all player seasons from 1986 to 2011 where a player missed and played at least 20 games. In particular, we’re interested in which player stats best predict whether a team will win or lose more often without him.By this measure, PER vastly undervalues steals. Because steals and baskets seem to be similarly valuable, and there are so many more baskets than steals in a game, it’s hard to see how steals can be all that important. But those steals hold additional value when we predict the impact of the players who get them. A lot more value. So much so that a player’s steals per game is more important to evaluating his worth than his ability to score points, even though steals are so much rarer.To illustrate this, I created a regression using each player’s box score stats (points, rebounds, assists, blocks, steals and turnovers) to predict how much teams would suffer when someone couldn’t play.5As measured by his difference in SRS (simple rating system, or average margin of victory/defeat adjusted for strength of schedule) with or without him. By comparing the regression coefficients for each variable, we can see the relative predictive value of each (all else being equal). Because we’re particularly interested in how each stat compares with points scored, I’ve set the predictive value of a single marginal point as our unit of measure (that is, the predictive value of one point equals one, and something five times more predictive than a point is five, etc.). The results:Yes, this pretty much means a steal is “worth” as much as nine points. To put it more precisely: A marginal steal is weighted nine times more heavily when predicting a player’s impact than a marginal point.6At least when averaged over a sufficient number of games (about 15 or 20). Note that the weighting of steals in PER was approximately equal to a made two-point basket, or roughly equivalent to two PPG (off by nearly a factor of five). Value for turnovers is negative.For example, a player who averages 16 points and two steals per game is predicted (assuming all else is equal) to have a similar impact on his team’s success as one who averages 25 points but only one steal. If these players were on different teams and were both injured at the same time, we would expect their teams to have similar decreases in performance (on average).Steals have considerable intrinsic value. Not only do they kill an opponent’s possession, but a team’s ensuing possession — the one that started with the steal — often leads to fast-break scoring opportunities. But though this explains how a steal can be more valuable than a two-point basket, it doesn’t come close to explaining how we get from that to nine points.I’ve heard a lot of different theories about how steals can be so much more predictively valuable than they seem: Steals “cost” less than other stats,7This is most relevant to comparison between steals and points: Points cost you shots, which cost you possessions, which is why a bad shooter may get a lot of points while hurting his team’s offense. Steals come at a cost as well: By gambling on defense, you sometimes give up a better shot if you fail. But, all things considered, they are probably closer to being “free” than points. or players who get more steals might also play better defense, or maybe steals are just a product of, as pundits like to call it, high basketball IQ. These are all worth considering and may be true to various degrees, but I think there’s a subtler — yet extremely important — explanation.Think about all that occurs in a basketball game — no matter who is playing, there will be plenty of points, rebounds and assists to go around. But some things only happen because somebody makes them happen. If you replaced a player with someone less skilled at that particular thing, it wouldn’t just go to somebody else. It wouldn’t occur at all. Steals are disproportionately those kinds of things.Most people vastly underestimate how much a player’s box score stats are a function of that player’s role and style of play, as opposed to his tangible contribution to his team’s performance. A player averaging one more point per game than another doesn’t actually mean his team scores one more point per game as a result of his presence. He may be shooting more than he should and hurting his team’s offense. Similarly, one player getting a lot of rebounds doesn’t make his team a good rebounding team: He may be getting rebounds that his team could have gotten without him.What we are looking for is a kind of statistical “irreplaceability.” If a player produces one more X (point, rebound, steal, etc.) for his team, and is then taken from the team (by injury, suspension, trade, etc.), how much of that stat does his team really lose? How much of it can be replaced?I tested for this by running a series of regressions using each player’s box score stats (points, rebounds, assists, etc.) to predict how much teams would suffer without a player in each particular area. In other words, for a player who averages X points, Y rebounds, Z assists, etc., how much does his team’s scoring decrease when he’s out? How much does its rebounding decrease? The way I’ve set it up, a stat’s irreplaceability will roughly run from zero (completely replaceable) to one (completely irreplaceable).8I was going to call this “Beyoncé Value” in honor of the singer’s hit song “Irreplaceable,” but editors correctly pointed out that the song title was ironic, and steals actually are irreplaceable. Let’s visualize it like so:9For this case, I ran separate regressions to the WOWY differential for each of the team’s PRABS statistics from all of the corresponding player stats. In a linear regression, the “irreplaceability value” is the coefficient for each variable in its own regression (e.g. player PPG coefficient in the regression to team PPG). Note that while the value approximates a percentage, nothing precludes values below zero or above 1. So, look at the points-per-game column. Suppose a player averages one more point per game than another player. His team is likely to average only an additional .17 points with him on the floor because points are 83 percent replaceable. It would take almost six points of his scoring to add one additional point to his team’s tally.For steals, the picture is much different. If a player averages one more steal than another player (say 2.5 steals per game instead of 1.5) his team is likely to average .96 more steals than it would without him (if all else stayed equal). That’s why, as an individual player action, steals are much more irreplaceable than points.Basketball is a game of high scores and small margins. The best team ever — the 1995-96 Chicago Bulls — only won by an average of 12 points per game, and I’d be surprised if more than a handful of players have ever been worth half that on their own (maybe Michael Jordan, probably LeBron James). With steals 96 percent “irreplaceable,” and each worth a couple of points, one extra steal per game puts a good player well on his way to being an excellent one.With this in mind, it’s worth taking another look at Rubio, the quirky sidekick to MVP candidate Love. Rubio seems deficient at the game’s central skill (putting the ball in the hoop) but is gifted at the one that matters to my model (thievery).It’s our good fortune that Rubio and Love have missed a number of games at different times, so we can check whether there’s anything to be gleaned by comparing team performance with and without them. Here are his and Love’s win percentages and average team margin of victory both together and separate since 2011-12:In other words, the Timberwolves have struggled to win games when either one of its duo out, and they’ve lost quite badly with both gone. Despite being an elite scorer and rebounder who is routinely ranked as one of the league’s top players, Love’s observable impact has been only marginally better than Rubio’s.10Note also that in the three years prior to Rubio’s arrival, Love had one of the worst runs that a theoretically great player has ever had. In the 214 games he played in that period, the Timberwolves won only 24.8 percent of their games and had an average margin of victory of -6.3. In other words, the sample of games in which the Timberwolves struggled with only Love on the floor is effectively much greater than the 33 in the table. So far, both are putting up elite numbers. The Timberwolves have played nearly seven points per game worse without Rubio in their lineup. That’s absurdly high. So high that I’d be surprised if either player’s numbers bore out in the long run. But it’s worth noting that, contrary to conventional wisdom, Rubio may be exceeding expectations.Taken alone, this comparison doesn’t answer the question of Rubio’s value, and it doesn’t prove that steals are as valuable as I think they are. But it’s powerfully consistent with that claim. More important, it’s a perfect example of how, even in a storm of complex, causally dynamic, massively intertwined data and information, sometimes odd little things that are known to be reliable and predictable are the most valuable.Editor’s note: A table in this article has been updated to include additional data from the past week. read more

Watch Game 7 Of The World Series With FiveThirtyEight By Reading Our

In less than a week, you may have heard, there’s a midterm election in the United States of America. This is sort of a big deal for us at FiveThirtyEight. Such a big deal that our estimable tech team of Jeremy Weinrib and Paul Schreiber arranged a fancy live-blogging platform so you can snuggle up next to us for hours on election night. It’ll be cozy.We’ve known for weeks that we’d need to give the platform a test drive, and we decided that we’d do that Wednesday, on the second night of the NBA season. We’d get together our crew of basketball writers (the ones who wrote our NBA team previews), buy some pizzas and use an algorithm to project whether Giannis Antetokounmpo has finally stopped growing.But as the San Francisco Giants discovered last night, Jake Peavy has a habit of ruining the best-laid plans.About the time Game 6 of the World Series passed a 95 percent win probability, we made the call to scuttle the NBA live blog. Instead, you’ll get to hang with us as we watch Game 7. We’ll argue that Jeremy Guthrie shouldn’t pitch more than three innings, locate where the Giants dynasty of the past five seasons would rank compared to others and, Yost-willing, debate the merits of the sacrifice bunt.It’s going to be great. Or a total disaster. Come and find out which. 8 p.m. EDT Wednesday. Here on FiveThirtyEight. read more

In The Spread Offense Era Can Wisconsin Rush Its Way To The

The football game played Saturday in Madison could have taken place 20 years ago. Wisconsin performed a complete takedown of Michigan, outclassing the Wolverines on both lines. The Badgers were unstoppable in the running game, piling up 359 yards on 57 carries. They pressured Michigan on 39.6 percent of dropbacks, negating the Wolverines’ speed at the skill positions. They led 28-0 at halftime and had the ball for more than 41 out of 60 minutes.In short, they played the game that Michigan coach Jim Harbaugh has always wanted his team to play. After he accepted his first head coaching job, at San Diego in 2004, Harbaugh told legendary Michigan coach Bo Schembechler that he would always have a fullback on his roster. As they shift toward spread concepts — Harbaugh brought in coordinator Josh Gattis last offseason to overhaul his ground-and-pound offense and replace it with a no-huddle attack as a last-ditch effort to break Michigan’s Ohio State curse — this year’s Wolverines don’t have a fullback. But Wisconsin has three, and one of them, John Chenal, scored a touchdown against Michigan. After Saturday’s rout and shutouts of South Florida and Central Michigan by a combined score of 110-0, Paul Chryst’s team is 3-0, No. 9 in the coaches’ poll and No. 8 in the AP poll. The Badgers appear to be the best version of their traditional hard-nosed, smashmouth selves. The question this year is: Is that good enough to make the College Football Playoff?In the spread-offense era, Wisconsin would be quite the party-crasher. The Badgers this season have averaged one snap every 31 seconds, playing at a slower tempo than any of the 20 playoff teams to date. Just 41.4 percent of their play calls are passes, fewer than any playoff finalist except Georgia in 2017. Chryst’s team has run more than half of its plays (118 of 224) from under center and huddled before 99.1 percent of its snaps. On its fourth play from scrimmage Saturday, Wisconsin faced fourth-and-inches from its own 34-yard line and came out in a jumbo formation it calls “14-Hippo” featuring seven offensive linemen, two tight ends, quarterback Jack Coan and running back Jonathan Taylor. “We slowly got to where we wanted to be, right?” Chryst told reporters afterward. “It worked.”Wisconsin has been playing this way for years; since 2005, the style has produced nine double-digit-win seasons and three Rose Bowl berths. But the Badgers have not played for a national championship in that time. They came closest in 2017, when they marched undefeated through the regular season but fell just short to Ohio State in the Big Ten championship game. That team, perhaps more than any other, illustrated the perils of playing a plodding style in this era. When it counted, Ohio State raced past Wisconsin for 57-yard and 84-yard touchdown passes in the first quarter, and the Badgers managed only one offensive touchdown in a 27-21 loss. When their defense gave them two final chances to take the lead in the fourth quarter, their offense finished those drives with a punt and an interception.This year’s team may already be different in one area: quarterback. From 2016 to 2018, Wisconsin started the inconsistent Alex Hornibrook, whose touchdown-to-interception ratio was just 1.42 and whose highest completion percentage in a season was 62.3 percent, good for 33rd among the top 100 quarterbacks. (The other two years he didn’t make the top 100 in completion percentage.) He threw crucial late interceptions in games such as that 2017 Big Ten championship and a 2016 trip to Michigan. Coan faced no such problems Saturday, when he rushed for two touchdowns. In Coan, Wisconsin might now have a capable quarterback to pair with its defense and Taylor, its Heisman candidate running back.This year’s Badgers have one opportunity that the 2017 version didn’t: a regular-season trip to Ohio State. Two seasons ago, Wisconsin did not play a top-15 team until the conference championship, and losing that game knocked the team out of playoff contention. This year, the Badgers can make a huge statement by beating the Buckeyes for the first time since 2010. Their schedule may even afford them two cracks at it: If they win all their other games, they could lose to the Buckeyes in their regular-season matchup next month, avenge that loss in the Big Ten title game and still make the playoff.If they can’t beat Ohio State, the Badgers will likely become another good team with a stout defense and an offense that’s serviceable but not flashy, potentially ending up in a New Year’s Six bowl but missing the playoff. And if Wisconsin can beat its old rival, Alabama and Clemson will likely be lying in wait, setting up the ultimate test of speed versus power.1So far this season, Alabama and Clemson are huddling before 40.4 percent and 34.4 percent of snaps, respectively. This decade has overwhelmingly favored speed. Old-school Wisconsin is hoping it can turn back the clock. read more

Womens volleyball OSU drops two matches in Michigan

Ohio State’s libero Valeria Leon passes a ball in the regional quarterfinal versus Washington on December 11, 2015. Credit: Ohio State AthleticsThe weekend wasn’t kind to the Ohio State women’s volleyball team, as they dropped two matches on the road against Michigan State and OSU’s main rival, Michigan. Up first were the No. 21 Michigan State Spartans, who silenced the No. 14 Buckeyes in a 3-0 sweep on Friday. OSU then travelled to Ann Arbor to take on the No. 23 Michigan Wolverines. The Buckeyes would only secure one set before falling to their foe, 3-1. The pair of losses comes on the heels of OSU’s upset over then-No. 1 Nebraska on October 1. After this weekend, the Buckeyes hold a 2-4 conference record. The team is 12-6 overall. Michigan StateOSU would be starting another new lineup against the Spartans. Sophomore outside hitter and regular starter Audra Appold was still out due to an injury that originated before the Buckeyes’ match against Northwestern on Sept. 28. The Buckeyes would be playing catch-up for the entire first set. Unforced errors forced OSU head coach Geoff Carlston to burn a timeout early on in the set. The Spartans would go on a 4-0 run to make the score 18-9 before Carlston used his remaining timeout for the first set. The Buckeyes couldn’t respond to the Michigan State offense and would drop the first set, 18-25. Much like the first, Michigan State would carry the lead for the entire second set. The Buckeyes pulled within reach of a tie mid-set until the Spartans started to gradually increase their separation. An OSU service error would secure the second set win for Michigan State. The third set would carry a different tune as the teams would see 10 tie scores and the Buckeyes would hold their first lead of the match. Senior middle blocker Taylor Sandbothe and junior outside hitter Luisa Schirmer combined for nine kills and sparked OSU’s offense. With the score tied at 21-21, it seemed like the third set could go to either team, but in the end it was the Spartans who would score the match point and complete the sweep of OSU, 25-23. Sophomore setter Taylor Hughes led the team in hitting percentages with .429. She also added 23 assists and combined for four blocks. MichiganIn front of a sold-out crowd in Ann Arbor, OSU would face their school rivals, Michigan. The Wolverines started the match out strong, jumping out to 6-0. After an OSU timeout, Sandbothe hammered down a kill and finally put the Buckeyes on the scoreboard. However, OSU errors would continue to dig a deep hole for the team. At 12-4, Michigan was boasting an errorless hitting game compared to five errors for OSU. The Buckeyes would score seven unanswered points later in the set to close the gap, but three costly OSU service errors would give the first set to Michigan, 25-20. The second set saw 13 tie scores, two lead changes, and proved to hold true to the meaning of the rivalry. Midway through the set, each team’s offensive numbers were explosive: .400 for the Buckeyes and .611 for the Wolverines. Schirmer put away seven kills alone in the second set. It was Michigan who would get the first set point at 24-23, followed by an OSU hitting error.  The Wolverines would have the advantage going into the third set, 2-0. OSU may have been going into the third with a disadvantage, but they weren’t going down easily. The Buckeyes took the early lead at 11-7 and forced a Michigan timeout after an ace by Hughes. OSU would get their first set point of the match from a kill by Sandbothe that would push the duel into four sets. The fourth set was a crowd thriller. OSU was able to come back from an 18-9 deficit to even the score at 21-21. The Buckeyes were able to fend off two Wolverine set points, but two skill Michigan plays would secure their match victory. Sandbothe and Schirmer paced the Buckeyes’ offense with 17 kills each, while senior libero Valeria León collected 23 digs. OSU will be back at St. John Arena for a rematch with the No. 3 Nebraska Huskers on Friday. read more

Volleyball welcomes pair of Big Ten foes

The Ohio State women’s volleyball team (13-2) faces two Big Ten teams at home this weekend.Among the 331 Division I teams, the Buckeyes fall just short of the top 25. Competition in the Big Ten is fierce, as seven of the top 25 teams are Big Ten schools.   The team faces Wisconsin on Friday at St. John Arena. Wisconsin (5-6) is struggling at the start of its season.The Iowa Hawkeyes (9-5) make their trip to St. John Arena Saturday.Both Wisconsin and Iowa are ranked ouside of the top 25, and Iowa recently beat the Badgers 3-1. “Wisconsin is a much bigger team, but Iowa is playing well and their win over Wisconsin gave them confidence,” OSU coach Geoff Carlston said.  The team has been anticipating these two Big Ten matchups. Carlston recognizes that early in the season each team is still getting into its own rhythm. The Buckeyes’ goal this week has been to focus on themselves instead of what the other team is bringing to the floor.   “We just need to find our offensive balance and outplay them on the defensive side of the game,” Carlston said.  Power players this year are outside hitter junior Katie Dull and middle blocker senior Kristen Dozier.  Both matches are set for 7 p.m. at St. John Arena. read more

Let the madness begin Breaking down the bracket

3. Coach Bo Ryan leads No. 4-seeded Wisconsin (23-8) into the NCAA Tournament for the 10th consecutive year. The Badgers will play the Atlantic Sun Conference champion, No. 13-seeded Belmont Bruins (30-4), in the first round. 3. After being in contention for a No. 1 seed for most of the season, No. 4 seed Texas fell after losing three of four games to unranked teams late in February. The Longhorns will take on No. 13 seed Oakland, who some have pegged to be a Cinderella team after winning the Summit League Championship. 5. Kemba Walker took No. 3 seed Connecticut to a five-games-in-five-days championship run to the Big East Tournament — a conference that sent 11 teams to the NCAA Tournament. The 6-foot-1 guard averaged 26 points per game in the conference tournament. UConn takes on No. 14 Bucknell, which won the Patriot League title.   Southwest Region 1. No. 1 seed Kansas is the deepest team in the nation, but at times loses focus defensively. Nevertheless, the Jayhawks are a legitimate title contender and are the overwhelming favorite to win the region. East Region 1. Ohio State, the No. 1 overall seed in the tournament, is the favorite, not only to win its region, but to be the last team standing come April 4. The Buckeyes should cruise until a potential matchup with No. 4 seed Kentucky in the Sweet 16. 2. No. 6 seed Xavier’s Tu Holloway is the best player you’ve never heard of. The Atlantic-10 Player of the Year was second in the A-10 in points, assists, steals and free-throw percentage. Holloway also has recorded two triple-doubles on the season, and scored 26 points in last season’s Elite 8 loss against Kansas State. 3. The best first-round tilt will take place between No. 8 seed George Mason and No. 9 seed Villanova. George Mason comes in winner of 16 of its last 17 games, while Villanova has lost five in a row and seven of its last nine. This game will feature two teams that look to push the tempo, evidenced by both squads’ 73-points-a-game averages. 4. No. 2 seed North Carolina is the only team in the region with enough size potentially to get OSU’s big men in foul trouble, and the talent to go shot for shot with the Buckeyes. However, the Tar Heels’ tournament inexperience is a major concern. 5. That said, OSU will win the East Region. The Buckeyes boast an impeccable combination of youth and experience, as well as perimeter shooting and inside play. That should be enough for them to reach their second Final Four in four years. West Region 1. No. 1 seed Duke likely needed its championship run in the ACC Tournament to secure a No. 1 seed after beating North Carolina, 75-58, in the conference title game. The Blue Devils benefited from Notre Dame falling to Louisville in the semifinals of the Big East Tournament. There’s speculation about whether star freshman guard Kyrie Irving’s toe injury will allow him to participate in the tournament. 4. No. 2 seed San Diego State was the second-to-last team in the country to remain undefeated, behind OSU. The Aztecs suffered their only two losses to BYU, but beat BYU, 72-54, in the Mountain West championship game Saturday. They take on No. 15 seed Northern Colorado, led by guard Devon Beitzel’s 21.4 points per game. 2. Purdue gets a favorable draw as a No. 3 seed. Look for its veteran leadership and scoring to carry it on a deep tournament run. 3. No. 10 seed Florida State has star player Chris Singleton returning from injury and the Seminoles’ defensive mindset allows them to match up with anyone. Notre Dame will have its hands full in round two. 4. Few expected Louisville to be a No. 4 seed come March. Don’t be surprised if Rick Pitino’s squad continues to exceed expectations. 4. Michigan State was named the Southeast region’s No. 10 seed after posting a 19-14 record and advancing the Big Ten Tournament’s semifinal round. The Spartans will play No. 7-seeded UCLA (22-10) in the first round. This is the Spartans’ 14th consecutive NCAA Tournament appearance. 2. No. 8 seed Michigan will take on the team that took out OSU last year in the Sweet 16, No. 9 seed Tennessee, in the first round. The Wolverines ranked 249th in the country in scoring and will face the Volunteers, led by 6-foot-7 Scotty Hopson’s 17.4 points per game. 2. 2010 NCAA Tournament runner-up Butler (23-9) is the No. 8 seed in the Southeast Region. The Bulldogs will play No. 9-seeded Old Dominion (27-6) in the first round. 5. Upsets in matches between 12th- and fifth-seeded teams are always trendy, but Atlantic 10 Tournament champion Richmond poses a legitimate threat to the region. Remember the name Justin Harper. The 6-foot-10 forward with 3-point range could make a name for himself. Southeast Region 1. UNC-Asheville will take on Arkansas-Little Rock on Tuesday in the Southeast Region’s play-in game. The winner will assume the region’s No. 16 seed and play No. 1-seeded Pittsburgh (27-5) in its next game. 5. The Florida Gators (26-7) were named the Southeast Region’s No. 2 seed. On Nov. 16, Ohio State defeated the Gators, 93-75, in Gainesville, Fla. read more

Ohio State mens tennis team makes racket on road to NCAA Championship

The Ohio State men’s tennis team’s dream of winning a national title is still alive after the Buckeyes defeated their opponents in the first two rounds of the NCAA Championship Tournament this weekend. OSU was able to shut out Notre Dame and Ball State, 4-0, respectively in the first and second rounds of the tournament. The team will now advance to the third round of the championship Thursday, when it will play Tulsa. Tulsa caused an upset in the bracket with a 4-3 victory against No. 13 seed Texas on Saturday. “They take everything seriously,” OSU coach Ty Tucker said of his team. The last four rounds of the tournament will be held at Stanford’s Taube Tennis Center. If the Buckeyes beat Tulsa in the Sweet Sixteen, they will play in their first matchup with a seeded opponent. Depending on the outcome of the other teams’ match, the Buckeyes will face either No. 5-seeded Baylor or No. 12-seeded UCLA. The Buckeyes didn’t play either team this season. OSU had a successful start to the tournament with home-court advantage. However, the team will now travel to an outdoor court, something the Buckeyes haven’t had much experience with yet this season. But the team isn’t completely unprepared. Redshirt sophomore Devin McCarthy said Tucker had the team outdoors, practicing as much as possible in the days before the tournament began. But, McCarthy added, the team still needs to play outside more. On Friday, the team got a little taste of what kind of weather it might meet in California when it played in humid, 80-degree weather against Ball State. “We have to take what we can get,” redshirt freshman Peter Kobelt said of Friday’s weather. “It’s the kind of stuff we’ll deal with if we go out to California.” Whatever weather the Buckeyes face in California, the team seems excited just to be competing. “It’s quite an experience,” senior Matt Allare said. “I’ve never been (to Stanford). I’m excited to see what it’s like.” “It’s a little bit different,” freshman Blaz Rola said. “You see a lot more excitement. … There’s more at stake, but to win any tournament is a big deal.” The Buckeyes play their next round at 6 p.m. Thursday. If they win, they will continue on the road to the National Championship by playing in the fourth round at 7 p.m. Saturday. read more

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