LAS VEGAS – Apparently, prison is not so comfortable for Floyd Mayweather Jr. Go figure.His lawyers are claiming that the undefeated champion boxer may never fight again if he’s not released from jail he entered earlier this month.The Las Vegas Review-Journal reports Mayweather’s lawyers filed an emergency motion Monday asking that he serve the remainder of his three-month jail sentence in house arrest.Lawyer Richard Wright says Mayweather’s health is deteriorating by being confined to his cell 23 hours a day and restricted from working out. Jailers are keeping him from the general inmate population because of his celebrity status.The motion says the boxer’s doctor believes the conditions could do irreversible damage to Mayweather’s physique.Bill Cassell, a spokesman for Vegas police department that oversees the jail, confirmed the boxer is in administrative segreation, confined to his cell all but one hour a day. The 60 minutes a day he gets is by himself is in a recreation area that does not include training facilities, such as weights.“He’s in adminstrative segreation for his safety and the safety of the other prisoners,” Cassell said. “Whether that changes is up to the judge today.”
This story appears in the May 6, 2013, issue of Sports Illustrated.I’m a 34-year-old NBA center. I’m black. And I’m gay.I didn’t set out to be the first openly gay athlete playing in a major American team sport. But since I am, I’m happy to start the conversation. I wish I wasn’t the kid in the classroom raising his hand and saying, “I’m different.” If I had my way, someone else would have already done this. Nobody has, which is why I’m raising my hand.My journey of self-discovery and self-acknowledgment began in my hometown of Los Angeles and has taken me through two state high school championships, the NCAA Final Four and the Elite Eight, and nine playoffs in 12 NBA seasons.I’ve played for six pro teams and have appeared in two NBA Finals. Ever heard of a parlor game called Three Degrees of Jason Collins? If you’re in the league, and I haven’t been your teammate, I surely have been one of your teammates’ teammates. Or one of your teammates’ teammates’ teammates.Now I’m a free agent, literally and figuratively. I’ve reached that enviable state in life in which I can do pretty much what I want. And what I want is to continue to play basketball. I still love the game and I still have something to offer. My coaches and teammates recognize that. At the same time, I want to be genuine and authentic and truthful.Why am I coming out now? Well, I started thinking about this in 2011 during the NBA player lockout. I’m a creature of routine. When the regular season ends, I immediately dedicate myself to getting game-ready for the opener of the next campaign in the fall. But the lockout wreaked havoc on my habits and forced me to confront who I really am and what I really want. With the season delayed, I trained and worked out. But I lacked the distraction that basketball had always provided.The first relative I came out to was my aunt Teri, a superior court judge in San Francisco. Her reaction surprised me. “I’ve known you were gay for years,” she said. From that moment on, I was comfortable in my own skin. In her presence, I ignored my censor button for the first time. She gave me support. The relief I felt was a sweet release. Imagine you’re in the oven, baking. Some of us know and accept our sexuality right away and some need more time to cook. I should know — I baked for 33 years.When I was younger I dated women. I even got engaged. I thought I had to live a certain way. I thought I needed to marry a woman and raise kids with her. I kept telling myself the sky was red, but I always knew it was blue.I realized I needed to go public when Joe Kennedy, my old roommate at Stanford and now a Massachusetts congressman, told me he had just marched in Boston’s 2012 Gay Pride Parade. I’m seldom jealous of others, but hearing what Joe had done filled me with envy. I was proud of him for participating, but angry that as a closeted gay man I couldn’t even cheer my straight friend on as a spectator.If I’d been questioned, I would have concocted half-truths. What a shame to have to lie at a celebration of pride. I want to do the right thing and not hide anymore. I want to march for tolerance, acceptance and understanding. I want to take a stand and say, “Me, too.”Believe it or not, my family has had bigger shocks. Strange as it seems today, my parents expected only one child in 1978. Me. When I came out (for the first time) the doctors congratulated my mother on her healthy, 7-pound, 1-ounce baby boy.“Wait!” said a nurse. “Here comes another one!” The other one, who arrived eight minutes later and three ounces heavier, was Jarron. He’s followed me ever since, to Stanford and to the NBA, and as the ever-so-slightly older brother I’ve looked out for him.I had a happy childhood in the suburbs of L.A. My parents instilled in us an appreciation of history, art and, most important, Motown. Jarron and I weren’t allowed to listen to rap until we were 12. After our birthday, I dashed to Target and bought DJ Quik’s album Quik Is the Name. I memorized every line. It was around this time that I began noticing subtle differences between Jarron and me. Our twinness was no longer synchronized. I couldn’t identify with his attraction to girls.Read more: Sports Illustrated
Image by NEIL MILLERA Jackie Robinson statue outside MCU Park in Coney Island has been vandalized.The statue was spray painted with swastikas and racist slur that read: “Heil Hitler,” “Die n—-r,” “F–k n—-r” and “F–k Jackie Robinson.”Brooklyn Cyclones team officials said they made the discovery on Wednesday morning.“Immediately we went into action to try to clean it and remove the graffiti,” said Billy Harner, the team’s director of communications. “We have video surveillance of the area, we’re looking at the tapes to investigate exactly what took place.”The sports organization had the slurs covered with duct tape as fans arrived at the park for the team’s 11am ET game. They also plan to remove the paint by using solution at a later time. Police are currently investigating the crime.“This is being treated as a bias crime,” detective John Nevandro of the 60th precinct said in a statement. “Hate Crimes will investigate the incident.”The statue depicts teammates Robinson and Pee Wee Reese during the 1947 season.“The statue is a symbol of tolerance,” said Harner. “It’s an absolute tragedy that someone would deface it the way they did.”The Brooklyn Cyclones are the minor league team of the New York Mets.
By 2010 — a down year by New England’s standards but the first year of the Pats’ experiment with two-tight end jumbo packages — 31 percent of Brady’s passing yards were coming on play-action passes; rookies Gronkowski and Hernandez helped the Patriots to the highest rate in the league, a stark contrast from 2006 when Brady ranked 29th out of 32 qualified passers.The unpredictability goes for other supposed Brady calling cards, like consistent yards after the catch. Brady typically rates among the league leaders in YAC per completion, but he dipped to 24th out of 33 qualified passers in 2014, a season in which the Pats went 12-4 and won the Super Bowl.There’s no telling how long Brady will keep making it deep into the postseason, but just as compelling as wondering how long the old man can keep this up is waiting to see what sort of offense he’ll bring with him if he does.VIDEO: The Patriots better worry about Julio Jones Related: Hot Takedown When the New England Patriots upset the St. Louis Rams in Tom Brady’s first Super Bowl, way back in 2002, Brady was throwing passes to Kevin Faulk, Troy Brown and David Patten. By 2008, he was throwing to the greatest wide receiver of all time, backed by a deep, talented receiving corps. Once Randy Moss left town, the Pats retooled with perhaps the best tight end in NFL history — and again once that tight end was repeatedly lost to injury. These were all very different players with very different strengths, but the Patriots found success with all of them.One of the more remarkable things about Brady appearing in his seventh Super Bowl on Sunday against the Atlanta Falcons at age 39 is how many different styles of offense he has found success in. It’s rare enough to find a quarterback who can make all of the throws, let alone one who can do it to so many different players with so many different game plans.Brady isn’t going to beat anyone with his legs (although he’s an effective sneaker), but the Patriots are unmatched in tweaking their offensive strategy for the personnel on hand. New England has had great offensive seasons in which over 70 percent of passing yards went to wide receivers, a high rate for the NFL. The Patriots have also had great seasons when WRs caught half of their yards or less, a rarity in the league. Five-time Pro Bowl wide receiver Wes Welker was greatly responsible for some of the Patriots’ most concentrated (and successful) passing seasons, eating up yards alongside Moss and Gronkowski. During his four best years, the Patriots were truly a star-driven passing offense, with fourth and fifth options like Jabar Gaffney and Danny Woodhead making a comparatively small impact in the passing game. In 2011, New England’s most top-heavy season of the Brady era, Welker, Gronkowski and Aaron Hernandez accounted for 33 of Brady’s 39 receiving touchdowns and over 70 percent of the team’s receiving yards.But the Pats don’t need stars in order to succeed. In 2005, Deion Branch, David Givens and Troy Brown caught just a little over 50 percent of the Pats’ receiving yards, with Ben Watson, Tim Dwight and Kevin Faulk adding contributions. An injury to Edelman in 2015 led to a similar situation – Gronk was the only player to crack 700 receiving yards, but eight players had at least 250. Of course, it helps to have some talent — the 2006 Patriots didn’t get much happening in their passing game with Reche Caldwell as WR1.That success with diverse sets of receivers has also come with diverse sets of game plans. Unlike, say, Packers quarterback Aaron Rodgers rolling left or Cardinals signal-caller Carson Palmer gunning the deep ball, conventional wisdom says that Brady’s game morphs to fit his personnel.One way we can test whether that’s true is to look at whether the things Brady was known for early in his career have continued to be central to his game as his career has progressed. At one point, the nearest thing he had to a specialty (besides those QB sneaks) was the play-action pass. Play-action is an effective part of any offense, and certain quarterbacks tend to rely on it more than others. But while Brady mastered it alongside future Hall of Famers Peyton Manning and Drew Brees, it has drifted in and out of his game over the years.ESPN’s more advanced passing data only goes back as far as 2006, after Brady had already established himself as a top quarterback — but a year before Moss and Welker showed up and the Pats began breaking passing records. Even picking up then, we can see drastic shifts in how often the Pats have gone to the run fake and how much of the passing offense it has made up. The 2016 Pats are light on wide receivers, although not exceptionally so for a Brady-led team. Julian Edelman topped 1,000 yards, and Chris Hogan established himself as a useful second receiver, but Danny Amendola mostly disappeared (putting up 243 yards in 12 games), and Malcolm Mitchell wasn’t exactly devastating. Meanwhile, tight ends Martellus Bennett and Rob Gronkowski (in eight games) combined for 1,241 yards, while running back James White chipped in another 551 in receiving.But, thanks in part to the Patriots, these positional distinctions don’t mean as much as they used to. Gronk isn’t a WR like Randy Moss was, but he is still more or less used as a receiver (a distinction that is, as Seahawks pass-catcher Jimmy Graham found, worth a lot of money in the pay scale). Perhaps a better way of looking at Brady’s versatility is how he does in the presence or absence of top targets. And while the Pats’ best seasons have come with major weapons at their disposal, they do just fine without them, thank you. Hot Takedown’s Super Bowl Special
After several weeks of involved trade discussions that would send prized Miami Marlins slugger Giancarlo Stanton to either the San Francisco Giants or St. Louis Cardinals, the baseball world was thrown a curveball Friday when it was reported that Stanton rejected both deals — and that the New York Yankees had swooped into the bidding. According to multiple reports, and assuming Stanton approves the deal, the Yankees had done on Saturday what the Giants and Cards couldn’t: They reeled in the game’s top power hitter.There were only two hitters last season who hit more than 50 home runs in MLB. Now, the Yankees have both of them: Stanton and fellow right-handed behemoth Aaron Judge. There’s reason to think Stanton will like hitting in Yankee Stadium as much as his new teammate. According to The Baseball Gauge’s park adjustments, Marlins Park was the third-most-difficult home run-hitting park for right-handed batters last season, which had the effect of depressing righty homers by about 20 percent relative to an average MLB ballpark.1The full-season park factor listed by The Baseball Gauge is 0.90, implying a 10 percent drop, but that number also reflects that a team plays half its games on the road, in (presumably) neutral parks. So the effect in Marlins home games alone would be about 20 percent. You read that right: Stanton smashed an MLB-leading 59 bombs — the most in baseball since 2001 — and took a serious run at Roger Maris’s pre-steroids HR record despite playing in one of the game’s most difficult parks for right-handed power hitters. There’s a reason Stanton was named NL MVP even though his team finished 20 games out of first place — it was one of the great individual seasons of this millennium.If you use The Baseball Gauge’s adjustment and extrapolate Stanton’s 2017 homers to a typical park, he’d project to have hit about 66 homers — easily shattering Maris’s mark. What’s more, Yankee Stadium ranked as the third-most-favorable park in baseball for right-handed home run hitters last season. Continuing our exercise above to project Stanton’s season into Yankee Stadium, he would figure to have hit around 73 homers (!!!) if he’d played in the Bronx instead of Miami. Now, the obvious caveats apply: Park factors are imperfect measurements that don’t account for each park’s exact dimensions, instead inferring the effect in a somewhat noisy way by looking at the change in home runs between a team’s home and road games. But even so, Stanton is probably going to get some kind of assist in his power numbers simply by upgrading his park situation.The real question for the Yankees is whether that boost will be enough to offset the tug of regression to the mean. Stanton had the best season of his career in 2017, and not just in the HR column, where he set a new career high by 22 homers. He also reached new career marks in isolated power, strikeout rate, on-base plus slugging and wins above replacement,2Using an average of the WAR models found at Baseball-Reference.com and FanGraphs. in addition to playing 150 games in a season for the first time since 2011. There’s a very good chance that last season was the best we’ll ever see out of Stanton, who still has at least 10 years and $295 million left on his gargantuan contract. It would be unfair to expect him to reproduce anything close to that level of performance, particularly given his history of injuries.According to WAR, Stanton was worth 7.2 wins at age 27 last season, the first time he ever broke the seven-win barrier in a single season. Since 1920, 66 hitters have cracked 7 WAR for the first time between the ages of 25 and 29 (provided they also put up at least 20 career WAR from their rookie season through their breakout season).3Stanton has 34.6 career WAR through 2017. Those players had that big year at an average age of 27.2 — roughly the same as Stanton last year — so they make for a good sample from which we can draw a comparison for Stanton’s next few seasons. Comparable players*276628.06.027.65.04.44.13.63.020.2 *Average for 66 comparable players.Sources: Baseball-Reference.com, FanGraphs PLAYERAGEPAWARPREV. HIGHCAREER WARYR+1YR+2YR+3YR+4YR+5NEXT 5 YRS. What’s in store for Giancarlo Stanton’s Yankees career?For players whose first 7-WAR season came between ages 25-29, average statistics in that season and each of the next five seasons, 1920-2017 IN FIRST 7-WAR SEASONWAR IN… G. Stanton276927.26.434.6?????? For our historical group — which includes the likes of Frank Robinson, Manny Ramirez and Tony Gwynn — the drop was relatively steep from their career-best season. On average, they fell from 8.0 WAR that year to 5.0 the following season, with the total diminishing over each of the next five years in a predictable aging pattern. Only 10 of the 66 ever had another season as good as their breakout campaign. Granted, Stanton’s big year was slightly less out of place with the rest of his career, so he’ll probably feel the pull of regression a bit less than other players might. And a batter who produces between 3 and 5 WAR is no bum — quite to the contrary, 5 WAR is roughly the border where All-Star seasons start to take shape.Plus, the Yankees might not even need Stanton to reproduce his 2017 in order to have a great season next year: Their run differential suggests they were roughly as good as the 104-win L.A. Dodgers last year, despite winning “only” 91 games. New York would have been formidable without Stanton, and with him (plus Judge, Gary Sanchez and others), they’ll be a right-handed power-hitting squad the likes of which the game may never have seen before.But at the same time, Stanton will probably not reach the heights of his performance from 2017 ever again — meaning the Yankees are getting a very good player but probably not one with perennial MVP potential. After all, there’s a reason they call it a “career year”: You only get one of them per customer.Either way, after several relatively quiet offseasons, general manager Brian Cashman and the Yankees seem to be returning to their big-ticket superstar roots. Now we’ll see if they can also revive the tradition of winning World Series.
Last week, prompted by ESPN’s new “30 for 30” documentary on the “Bad Boys” Detroit Pistons, I examined the question of just how “bad” the Bad Boys really were. In that piece, I used relative technical foul rates as a proxy for “badness” to establish that the Bad Boys Pistons teams did, indeed, deserve that moniker. Their two championship squads were two of the “baddest” teams in the past few decades, earning more technical fouls relative to their peers than any other teams since 1982. But one question lingers: Were they so good because they were so bad, or in spite of it?To find out, I looked at 30 years’ worth of the league’s correlation between technical fouls and winning. Technicals are the NBA’s official in-game punishment for conduct that the league and officials deem “unsportsmanlike” (short of a flagrant foul), which is why we’re using it as our proxy for badness.1In the Bad Boys Era, what are now flagrant fouls were mostly just technical fouls, and didn’t carry the extra penalty they do today. They, of course, have the immediate and measurable result of giving the other team one free throw by the shooter of its choice — worth around .85 points on average.2There’s also a minor effect of sometimes adding time to the opponent’s shot clock.Despite that negative consequence, teams that get more technical fouls than average tend to be pretty good. What’s more, the more technicals they earn, the more likely they are to be even better.Here’s a plot of the number of technical fouls (badness) a team had relative to the league average that year against its win percentage (goodness). The data below is pulled from all team seasons since 1982-83,3Limited to teams for which we have at least 10,000 combined minutes worth of data. showing only those that were badder than average.Look at the red dots, which are rolling 25-team averages. As the teams get more techs — or get badder — their winning percentages increase. That’s intriguing, as is the fact that the top 26 baddest teams in the data set all had winning records. Overall, 63 percent of these bad teams were good enough to have a winning record, and the top 100 of them had an average winning percentage of 60.3 percent.4The correlation between technical rate and win percentage is .27, which is pretty high for any metric based on only one stat.But finding a relationship in one season isn’t enough. The real test is whether the metric predicts performance in other seasons.5This is called taking your test “out of sample,” which separates cause and effect. Note, though, that it doesn’t necessarily tell you which is which. Below you’ll find a graph showing how technical fouls predict team strength in neighboring seasons, and how they compare to a variety of other popular metrics. For strength, we’ll use SRS, or “Simple Rating System,” which is a team’s average margin of victory adjusted for strength of schedule6The technicals per game metric I used is calculated relative to each season, while the other metrics are not. This gives it a slight advantage.:Effective field goal percentage comes out on top of this group, but technical foul rate holds its own, coming out as a better predictor of past or future team strength than stats stalwarts like points per game or rebounding percentage.7Also, technicals are more positively predictive than turnovers are negatively predictive, which is fascinating but beyond the scope of this article.That’s a bit wacky — the technical foul, remember, can’t provide value directly, because it gives up .85 points (on average) to the opposition. From where I sit, then, there are two potential kinds of explanation: Explanations that embrace the nasty. These would argue that teams that get more technical fouls are better because the behavior that leads to the technicals (i.e., bad behavior) likely provides more benefit than the occasional .85 points that it costs.8OK, actually there’s a third line of thinking, which is that technical fouls don’t cost the .85 points that we think they do because, say, referees overcompensate for calling technicals by giving teams better calls later in the game. But for all intents and purposes, I’ll treat those as part of the second theory. In baseball, high/inside pitches used to brush batters off the plate usually result in balls or sometimes even hit batters, but are commonly believed to be worth it (whether they actually are or not, I don’t know). For what it’s worth, I checked a boatload of possible confounding variables and combinations thereof, such as home/away (53 percent of technicals go to the away team); ahead/behind (57 percent go to the trailing team); and playoffs/regular season (if it were strictly a matter of effort, we would expect a difference when all teams have equal incentives to play hard. No major differences found). Coaching technicals appear to be at least as predictive as player technicals. If there’s a correlation between aggressive play and winning and aggressive coaching and winning, Occam’s Razor suggests that you should favor a single theory that explains both phenomena, such as that an aggressive ethos (which applies equally to coaches and their players) causes winning. In football, I’ve found that rookie quarterbacks who throw more interceptions (all else being equal) often have more productive careers. In basketball, offensive rebounds have a potentially similar problem from the opposite direction: While apparently a good thing, in quantity they signal that a team doesn’t shoot very well. In poker, a too-high showdown win percentage likely indicates that a player doesn’t bluff enough and/or doesn’t make enough marginal calls. So far my research hasn’t turned up any smoking gun proving the case one way or the other, but on balance I’d say the results are more consistent with the second option: Technical fouls exist to deter certain types of unsportsmanlike behavior, but if those behaviors are broadly advantageous (by intimidating or hurting the opponent, for example), they could be “priced incorrectly” at only (roughly) -.85 points each.9Compare it to the deterrence problem: In order to coerce different behavior, things have to be punished at a rate much worse than their actual effect.That something ostensibly negative can ultimately be predictive of something positive (or vice versa) isn’t an unheard of dynamic in sports. For instance: Not all good teams get a lot of technical fouls (the San Antonio Spurs, for example, consistently rank near the bottom of the league), but the vast majority of teams that get a lot of technical fouls are good. Of the 27 teams with the best winning percentages since 1982, two-thirds (18) have had more technical fouls than the league average at the time. (Compare that to the top 26 technical-getting teams having winning records.) But it’s unusual in basketball for an event with a negative impact to have a positive correlation with team strength. Take a look at some other things that have a direct impact on the game that’s similar to that of technical fouls (slightly above or below -1 point each):If everything else were equal, we would probably expect technicals to be in the same range as turnovers or steals, so the total gap from where they ought to be based on in-game value and where they actually are, predictively, is massive.10Note the gap between opponent offensive and defensive rebounds is smaller, even though there’s a straightforward reason that offensive rebounds are a mixed blessing (because it means the team is missing more shots).But even if we’re satisfied that technicals can predict wins, there’s still something we haven’t considered yet: Wins may predict technicals.11It’s like the Euthyphro question, but for sports gods: Are technicals good because the sports gods love them, or do the sports gods love technicals because they’re good? This theory has a few possible scenarios associated with it, such as: Teams that are in contention are playing hard all the time — so hard that they occasionally earn a technical — while teams that are out of contention don’t really care enough to do “whatever it takes” to win.That kind of explanation is intuitively appealing, both because the scenario has a plausible ring to it and because it’s the sort of unsexy answer you often find when you try to explain a strange result.To test this theory, I looked at play-by-play data over the last four years, which breaks fouls — including technicals — down by type. That yielded 1,963 player techs, 422 coach techs, 278 flagrants (similar to the technical, but with a much harsher punishment), and 2,448 three-second violations.12For the data set I used below, I also applied a number of filters: I filtered out the fourth quarter because variance is too great and tactical considerations trump other things. I also dropped hanging, taunting, non-unsportsmanlike and team technical fouls because their numbers are too small to break out, and I’d like to keep the main-line group as homogenous as possible.I combined all that with in-game win percentage calculations provided by Dean Oliver of ESPN Stats & Info, estimating the foul-committing team’s chances of winning before and after the foul (including the resulting free-throw).13I also duplicated all of this research using margin of victory so as not to rely entirely on the predictive algorithm, and the results were virtually identical. We’re interested in the difference between what that foul did to a team’s projected results and its actual results.Averaging across all plays, we can represent the results of this comparison in a slope chart that shows how the team’s chances should have changed in that moment, and how often it actually ended up winning. Take note of those two (well, four) lines for player and coach techs. Both player and coach technicals ostensibly cost teams about a 1.8 percent chance of winning the game, which is what we would expect based on the surrendered free throw. But the actual win percentages of technical-foul-getting teams appear much higher than we would expect. Teams ended up winning 2.1 percent more often than expected after player techs, and 3.8 percent more often than expected after coach techs.14Flagrant fouls don’t do as well, though they include a harsher penalty, including the possibility of the player being ejected.While this result supports our finding that technical fouls predict winning over an even larger number of observations, it’s also consistent with either type of explanation for why this is so. If there were any bias in how technical fouls are distributed — as suggested by the “wins predict technicals” theory — unfortunately it would still bias these results.But there’s something we can do to avoid that. Instead of computing the averages in that chart across every single foul, we can compute them on a team-by-team basis first, and then average the result across all teams equally — treating each team’s results as one data point regardless of how many technical fouls it received. That helps us avoid potentially skewed data if different types of teams (like winning teams) are more likely to get technicals in the first place. When we do that, here’s what we get (the new chart is on the right, with the old one on the left for comparison’s sake):Lo and behold, they’re extremely similar! Teams tend to win 1.4 percent more often when their players get a tech, and a whopping 5.5 percent more often when their coaches do. That similarity broadly suggests that “bad” (technicals) begets “good” (winning), rather than the other way around.To illustrate: If one great team, let’s call it SuperBad, earned every technical foul every year, but by virtue of being a great team won 5 percent more often than its expected win percentage would suggest, that would show up as a 5 percent gain in the chart on the left. (That’s because each time a team got a technical it won 5 percent more often, even though it was the same team every time, and even if the winning was unrelated.) But when averaged across all 30 teams in the league, it would only show a 0.16 percent gain in the chart on the right (SuperBad team ran 5 percent above average when getting a technical, but the other 29 teams ran 0 percent better15OK, technically undefined in this example, so add epsilon if you must.). This would be a perfect “winning begets technicals” scenario.On the other hand, if every team got an equal share of the same number of technicals as our SuperBad team, and every time a team got a technical it won 5 percent more often than it would have otherwise, it would show up both as a 5 percent gain on the left and a 5 percent gain on the right. This would be a perfect “technicals beget winning” scenario.The charts above seem much much closer to this second “technicals beget winning” scenario, as there doesn’t appear to be much difference whether we aggregate by plays or by teams. Indeed, the main reason this isn’t a smoking gun is that the sample size for the right-hand chart is only 120 team seasons, which would normally be much too small to even attempt to draw conclusions about differences of only a couple of percentage points either way. But being so consistent with the much larger sample of the play-by-play chart is powerful corroboration.Here are a few other things that cut against the “winning predicts technicals” theory: Finally, let’s return to the question that kicked off the piece: Were the Bad Boys Pistons so good because they were so bad, or in spite of it?Based on what I’ve looked at so far, I’d say the former has the stronger case: While technical fouls can’t lead directly to winning, the types of behavior that lead to technical fouls just may. Explanations that avoid the nasty conclusion that unsportsmanlike play gives a team an advantage. For example, it could be that technical fouls are committed more often by teams that are already winning, or that winning teams and players just have a propensity to get more technical fouls, and are willing to absorb the cost.
In the eternally running discussion thread “Hey Bill” at billjamesonline.com, the website of sabermetric legend Bill James, the question came up of measuring the growth of sabermetric knowledge. James’s idea? Measure the extent to which teams are taking park factors into account when judging their rosters. But Tom Tango, author of “The Book,” offered another gauge: look at which teams are using good hitters in the No. 2 lineup slot.Traditionally, the two-hole was the domain of contact hitters with good bat control, with premiums placed on the ability to hit behind the runner, to sacrifice bunt, and to generally move the leadoff man over (even if it meant making an out). You can see this statistically: During Major League Baseball’s expansion era (1961-present), the No. 2 slot has the highest aggregate contact rate of any batting order position.But research by Tango and his compatriots suggests teams have been doing it wrong. After examining how important each batting event (single, double, walk, etc.) is to each lineup slot — based on factors such as how many runners are likely to be on base and how many outs they’re likely to hit with — the data says a team ought to bat its three best hitters in the No. 1, No. 2 and No. 4 slots, with the most balanced hitter occupying the two-hole. That’s a far cry from the conventional wisdom of slotting the best hitter either third or fourth, and putting a weak contact specialist at No. 2.So, if there are more good hitters in the second position, it’s a possible sign sabermetrics has penetrated the managerial mindset. But if there’s a pattern toward a more enlightened lineup card, it’s not detectable by looking at the average quality of No. 2 hitters (according to weighted runs created, known as wRC+) since the introduction of the designated hitter in 1973:If we take a five-year moving average to smooth out year-to-year variance above, it’s even clearer that we’re not in the golden age of great hitters batting second:Historically, the quality levels of MLB leadoff and No. 2 hitters tend to track with each other — and contra the performances of third and fourth hitters. (Meanwhile, Nos. 5 and 6 have stayed fairly stable over the years, with the five slot outproducing six by a decent amount.) The good news is that it appears the two-hole has emerged from the dark ages of the mid-1990s to the mid-2000s, when slot Nos. 3 and 4 vastly outpaced Nos. 1 and 2.It may not be coincidental that the bleakest of times for the No. 2 spot came during MLB’s so-called steroid era. The stat we’re using, wRC+, compares a player’s per-plate appearance productivity against the average of all hitters, and the power hitters who frequently bat third and fourth may have received the benefits of performance-enhancing drugs at a greater rate than the overall population of MLB batters. (This would cause No. 2 hitters to move backward relative to the overall average, even if they themselves saw no change in talent.) With the specter of performance-enhancing drugs reduced in today’s game, the gap between hitter No. 2 and Nos. 3 and 4 has returned to its long-term norm.Still, today’s two-hole batters lag behind those of the halcyon late 1980s and early 1990s, when players such as Ryne Sandberg, Tony Gwynn, Wade Boggs, Roberto Alomar, Julio Franco and Lou Whitaker were doing a large share of their damage from the second spot in the lineup. It’s plausible that the conditions of the game back then simply favored the traditional archetype of the No. 2 hitter more (batting averages were higher, as was the ratio of on-base percentage to slugging), but today’s managers also don’t appear to be moving toward the sabermetric ideal of penciling the team’s best hitter into the No. 2 spot.Sabermetrics has come a long way since the first analysts began tinkering with mathematical models, and there are certainly places where statistical thinking has made its way onto the field (for example, the explosion of defensive shifts in today’s game is rooted in probability theory regarding where a batter is most likely to hit the ball). But when it comes to the two-hole, baseball’s decision-makers still have a bit of a climb ahead of them.
Alabama 12-1212100%>99% ▲ 2132% North Carolina 11-11091542%10% ▲ 211% Alabama 12-1212100%>99% ▲ 2128% Clemson 12-015758%76% ▲ 2114% Stanford 10-2761148%7% ▼ 6a1% Oklahoma 11-1331100%>99% ▲ 2140% Ohio State 11-16230%7% ▼ 9a1% Stanford 11-27611100%6% ▼ 8a<1% Oklahoma 11-1351100%99% ▲ 2139% TeamCFPEloFPIConf. TitlePlayoffNat. Title Iowa 12-1415260%<1% ▼ 39<1% LIKELIHOOD OF BEING SEEDED… College Football Playoff (CFP) rankings as of Dec. 1. Includes completed games as of 8 p.m., Dec 5. Playoff probability changes are since Dec. 2; only changes greater than 5 percentage points are shown. UPDATE (Dec. 6, 1:03 a.m.): Despite all the articles FiveThirtyEight published about the different scenarios that could end the college football season, all that chaos never materialized. The playoff committee’s choice is clear: Clemson, Alabama, Oklahoma and Michigan State should be the four teams to make the playoff.In case you’re a visual learner, here are our model’s playoff projections: All that’s really unknown going into the final committee rankings on Sunday are the seeding it will give to the four teams. Our model projects those, too: RankingProbability of … Ohio State 11-16330%2% ▼ 14<1% Iowa 12-04142639%39% ▲ 213% Clemson92%9%<1%<1% RankingProbability of … Oklahoma<1<16632 Clemson has a 92 percent likelihood of snagging the top seed, a position they’ve held in every one of the committee’s rankings thus far. There’s an inertia to these things, and Clemson hasn’t done much to shake the committee’s faith. The 13-0 Tigers finish their season as the only undefeated FBS team, with impressive wins against Notre Dame, Florida State and North Carolina. (Phantom offsides calls presumably don’t factor into the committee’s seedings.)Alabama — the one-loss champions from the SEC — are next in line, fresh off a dominating win against Florida. Currently ranked No. 2 by the committee, the Tide have a 91 percent shot at the No. 2 seed (the most likely outcome), but could possibly leapfrog Clemson to be the playoff‘s top dog (a 9 percent chance). Either way, Alabama is in the top two.Where Oklahoma and Michigan State end up is less clear. The Sooners have been the No. 3 ranked team in the last two committee rankings, and despite an early-season loss to Texas, Oklahoma measures up well according to advanced metrics, like ESPN’s Football Power Index (FPI) and Chase Stuart’s Simple Rating System. It’s unknown, though, just how much the committee weighs these metrics, leaving Oklahoma with a 66 percent probability of landing in the No. 3 spot.Michigan State survived a thrilling challenge from Iowa in the Big Ten championship, capping an epic 22-play drive with a touchdown in the final minute. Michigan State has the opposite odds of Oklahoma — the Spartans are likely to be seeded No. 4, but could jump to No. 3 depending on the committee’s whims. Then again, the Spartans might not want the No. 3 slot — that likely means they’ll have to play Alabama instead of Clemson, and according to our metrics Alabama is better.If you take a look at that first chart again, you’ll see that Oklahoma is the favorite to win the national title at 39 percent despite likely being seeded third. The Sooners are the No. 1-rated team according to ESPN’s Football Power Index1FPI uses a slew of measures — such as game outcomes, margin of victory, strength of schedule, offensive and defensive team efficiencies — to predict outcomes. You can read more about how it’s constructed here, and why FiveThirtyEight uses it for our game simulations here., which our model uses to simulate game outcomes.2The FPI-based projections presented here may change. FPI rankings will be updated following Saturday’s games. The metrics are higher on the Sooners than the committee has been.One last note: Stanford fans: you may notice a 6 percent shot at the playoff. But that’s just the model being conservative. While the Cardinal played a tough schedule and easily won the Pac-12, it’s extremely unlikely they’ll make it into the playoff over Michigan State. There just wasn’t enough chaos this year. Ohio State<1<1<12 ORIGINAL POST (Dec. 5, 8:24 p.m.): The Tide are rolling into the College Football Playoff. After dominating Florida 29-15 to win the SEC championship, Alabama is a lock for the playoff according to FiveThirtyEight’s model — though you probably didn’t need fancy stats to tell you that.Below are our updated playoff odds following Alabama’s win but before the ACC, Pac-12 and Big Ten championship games began. As we’ve outlined before, our model sees both Oklahoma and the winner of Iowa vs. Michigan State as shoo-ins. Michigan St.<1<13260 Alabama991<1<1 Michigan St. 12-15214100%93% ▲ 3211% Now it’s just a matter of where the Tide will be ranked within the top four. Last week they were placed at No. 2, but after winning against Florida, our model gives them a 45 percent shot at the No. 1 slot. If Clemson wins, that number gets much lower. The Tide are good, but the committee is likely to keep an undefeated ACC champion as the top team.The path for Stanford and Ohio State, meanwhile, just got much more treacherous. Both teams’ odds fell to 7 percent. Following Alabama’s win, they each need Clemson to lose against North Carolina later tonight; and, of course, the Cardinal need to win the Pac-12 championship over USC to stay in contention. But even then it’s no sure thing.Speaking of North Carolina, the Tar Heels are down, but in better shape than other underdogs because of that last game against Clemson. Because there’s only one playoff slot available to them (Alabama and Oklahoma are now locks, as is the Big Ten winner) the Tar Heels’ odds fell, but only by four percentage points, down to 10 percent. However, if they can upset Clemson and win the ACC, those odds rise to 25 percent. So a UNC playoff berth would still make for a surprise on Sunday, but there’s a chance the committee will smile upon their conference championship and big win against the former No. 1 team in the country.Then again, there’s also a 43 percent chance the committee will put Clemson in the playoffs anyway. We’re only a few hours away from knowing more! Check back in late tonight after the final games for our last predictions before the committee releases its final rankings. NO. 1NO. 2NO. 3NO. 4 TeamCFPEloFPIConf. TitlePlayoffNat. Title Michigan St. 11-1541461%60% ▲ 217% College Football Playoff (CFP) rankings as of Dec. 1. Playoff probability changes are since Dec. 2; only changes greater than 5 percentage points are shown. Stanford<1<1<15 Clemson 13-0147100%>99% ▲ 2320% For those of you who want more nitty-gritty about our projections, check out our original methodology manifesto, as well as a methodology update from earlier this season.
As we approach the end of the NBA’s regular season, awards conversations are all the rage. As usual, the two most talked-about races are for Most Valuable Player and Rookie of the Year. Whether it’s “Get Up” or The Jump, Sports Illustrated or CBS or NBA TV, or even NBA players themselves, everyone’s got an opinion on who should take home the hardware at the end of the season.The Rookie of the Year debate, at this point, pretty much boils down to the Mavericks’ Luka Doncic, who stormed out of the gate and grabbed onto front-runner status fairly quickly, and the Hawks’ Trae Young, who started off terribly but has been shining during the season’s second half.But lost among this debate is this: The entire 2018 NBA rookie class — or at least the top five picks — deserves an award. Collectively, they are having the best debut season of any group of top five picks in more than 25 years.Doncic (pick No. 3) is carrying averages of 21.2 points, 7.7 rebounds and 5.9 assists per game while acting as the primary facilitator and scoring option in Dallas. He is only the second rookie in NBA history to average at least 20, 7, and 5 in those categories, and the other is Oscar Robertson, who did so during the 1960-61 season.The man whom Doncic was traded for on draft night,1The Hawks drafted Doncic and traded him to the Mavericks in exchange for Young and Dallas’s top-five protected 2019 first-round pick. Young, has been nearly as productive, albeit less consistent, in his debut season for Atlanta. Young’s season-long numbers of 19.0 points, 3.7 rebounds and 8.1 assists per game are strong.2He’s one of only three rookies to have gone for 19, 3 and 8 per game. Those numbers, though, are dragged down by his poor start to the year. Since the All-Star break, he’s averaging 25.0 points, 4.6 rebounds and 9.2 assists a night, with shooting numbers that are far better than those he was posting earlier in the season as he struggled to adjust to the NBA game.Two of the first five picks in a given draft looking this good, this early, would be impressive on its own; but Doncic and Young are not alone in their shining debuts. The other three players selected in the top five — the Suns’ DeAndre Ayton (No. 1), the Kings’ Marvin Bagley III (No. 2) and the Grizzlies’ Jaren Jackson Jr. (No. 4) — have each been pretty damned good this year too.Ayton has been a monster offensive force for Phoenix from Day 1, and he is already one of the league’s best post scorers and offensive rebounders. Among rotation players averaging at least 2 post-ups per game, per NBA.com, Ayton’s 1.03 points per play on post-ups ranks third, behind only Joel Embiid and LaMarcus Aldridge. Ayton’s offensive rebound rate, meanwhile, ranks 22nd among the 263 players who have qualified for the minutes per game leaderboard. And he’s been improving on defense throughout the season.Bagley is averaging 14.8 points and 7.4 rebounds per game off the bench for the surprisingly frisky Kings. And he’s been even better since returning from a five-game, injury-related absence in early March, posting 18.5 points and 8.2 rebounds a night with an improved shooting line. He has a diverse, varied face-up game and is working to stretch his jumper, and given his athleticism and quick feet, his defense could eventually come around as well.Memphis shut down Jackson in late-February due to a quad injury, but before his season ended he averaged 13.8 points, 4.7 rebounds and 2.3 combined steals and blocks in just 26 minutes a night. He did all that despite being, at 19 years old, the second-youngest player in the league.3The Lakers’ Isaac Bonga is about a month younger than Jackson, and Bonga has played less than 100 minutes this season. Jackson also knocked down 35.9 percent of his threes and carried an above-average usage rate and true shooting percentage, which is wildly impressive for a player whose primary contributions were expected to come on the defensive end of the floor.So how does this season’s top five stack up against past classes? The chart below plots the collective win shares and win shares per 48 minutes for the top five picks in each draft class from 1979 through 2018 (otherwise known as the three-point era) during their respective debut seasons. Note that only players who played during the season immediately following that year’s draft are counted in this analysis; because we’re looking at the top five picks as a class, if a player did not debut with the rest of his class, it doesn’t make much sense to count him along with the others. For example, Ben Simmons was the No. 1 overall pick in 2016, but he did not play during the 2016-17 season, so he counts for 0 minutes and 0 win shares toward the total of that draft class. Simmons was excellent as a rookie once he did step on the floor, but it also would not make sense to group him with the 2017 draft class, because he was not drafted in 2017. Likewise, the same logic applies to Simmons’s Sixers teammate Joel Embiid, who was drafted in 2014 but did not debut until two years later.4It also applies to Jonas Valanciunas (stayed in Europe for a year before coming over and joining the Raptors); Blake Griffin (injured); Ricky Rubio (Europe); Greg Oden (injured); Danny Ferry (went to Italy for a year because he refused to play for the Clippers); David Robinson (naval service); and tragically, Len Bias (an overdose-caused death). 1982WorthyCummingsWilkinsGarnettThompson0.129 The top-five picks in the 2018 draft are in HOF companyThe five NBA draft classes with the highest win shares per 48 minutes Year1st2nd3rd4th5thWS per 48 min 1979JohnsonGreenwoodCartwrightKelserMoncrief0.137 1992O’NealMourningLaettnerJacksonEllis0.118 Hall of Fame inductees in boldSource: Basketball-Reference.com Draft pick 2018AytonBagley IIIDoncicJackson Jr.Young0.102 1984OlajuwonBowieJordanPerkinsBarkley0.174 As you can see, the 2018 class fares extremely well in both win shares — which represent Basketball-Reference.com’s attempt to divvy up credit for team wins to the individual players on the team — and win shares per 48 minutes. The 21.1 win shares collectively accumulated by Ayton, Bagley, Doncic, Jackson and Young ranks eighth among the last 40 draft classes during their respective debut seasons, while their win shares per 48 average of 0.102 makes this class one of just six to exceed 0.100 win shares per 48.One of those six classes (2009) saw only three players actually take the floor during their debut season, thanks to an injury that knocked Blake Griffin out for the year and Ricky Rubio’s contract with Barcelona that kept him in Spain for two years before he arrived stateside. Hasheem Thabeet, James Harden and Tyreke Evans saw varying degrees of success during their respective rookie years and ended up posting a collective average of 0.108 win shares per 48 minutes, but they also combined for only 11.9 total win shares, far fewer than the other five classes that stand out in this analysis, each of which exceeded 20 total win shares.It’s worth noting, then, who was actually taken in the top five in those five NBA drafts (1984, 1979, 1982 and 1992). It’s also worth noting that just a single class between 1992 and 2018 saw its top five post a win shares per 48 average better than 0.100, meaning it’s been nearly a generation since we saw an actual top five class debut with a performance as good as the one we’re seeing from the most recent draft class. Among the 20 players selected in the top five of those four drafts, eight are currently in the Basketball Hall of Fame. Another four — Bill Cartwright, Sidney Moncrief, Terry Cummings and Christian Laettner — made at least one All-Star team during their career. And six more became long-term rotation players. Only Greg Kelser and Bill Garnett failed to pan out at all, as they wound up out of the league entirely within a few seasons.That’s an incredible hit rate of solid NBA players, and bodes well for what we should expect from Ayton, Bagley, Doncic, Jackson and Young in the future. It’s obviously far too early to predict that any of these players will be enshrined in Springfield one day, but the future certainly appears bright, and it seems likely that the 2018 draft class will be remembered as one of the best in quite some time.Check out our latest NBA predictions.
Ohio State’s baseball team didn’t end the season how it wanted to, but it wasn’t a total failure going to Tallahassee, Fla., for the NCAA Regional.The Buckeyes lost to Florida State 37-6 in a game the team would like to forget.Recruiting visits in Tallahassee for the Buckeyes were more successful than the actual baseball playing. Manager Bob Todd was able to snag a right-handed pitcher and an outfielder, both from the same high school.Outfielder Hunter Mayfield and pitcher Cole Brown both decided to leave Tallahassee to go north to play baseball.For Brown, the decision to come to OSU to play baseball was a relatively easy one. Brown said Mayfield committed to OSU and then the coaches started pursuing him.“I came up here on a visit,” Brown said. “I just loved everything about OSU.”Mayfield may have not been the reason for Brown to commit to OSU, but he has been helpful. Brown said that he and Mayfield have been friends since middle school.Mayfield shared the same feelings, saying that it was nice to know at least one person at the start.The Buckeyes will be making a series of trips back to Florida for tournaments over the next month and a half, and both are looking forward to those trips.Unfortunately for Brown, playing time to start off the season will be limited. In his senior year of high school Brown only made two appearances because of an injury to his shoulder.“I’m doing good. I am back to 100 percent,” Brown said. “Now I just need to get my velocity back.”Todd spoke on Brown’s injury, saying he is going to bring him along slowly. Giving him the needed rest and rehab now, Todd is hoping he will be able to be used late in the season.On the other hand, Mayfield said he has been told he might see some playing time early in the season. If he isn’t able to play, Mayfield said he is fine with that. He just wants to do whatever to help the team.Whether the two see playing time or not, they have one positive thing to look forward to. With all the snow that Columbus received over the past few weeks, both are ready for warmer weather.Brown said in Florida they get to play outside throughout the year, while here at OSU they have had to stick to practicing inside.