Receiver Plaxico Burress said in a radio interview earlier this week that, “no doubt about it,” he wants to play next season for the Carolina Panthers, who are quarterbacked by last year’s rookie of the year, Cam Newton.But Burress has received calls from other NFC South teams, the Panthers haven’t called yet, he told WFNZ in Charlotte on Monday.“My brother lives right there in Charlotte, my cousin lives there in Charlotte, and all my family is there in Columbia, South Carolina. It would just be a great situation all the way around and being close to everybody.“Not just that, but playing with Cam, who I think is one of the top five quarterbacks in all of football, and as long as I can remember, Steve Smith, has been one of my favorites. I just think it’s a great situation.”Burress told WFNZ that NFL teams are interested in him but he hasn’t found the right fit. In February, Burress had expressed a strong desire to sign with the Philadelphia Eagles, but there apparently doesn’t seem to be a match between the team and receiver, as he remains unsigned.“I just want to put myself in a position to go out and have success and to make the guys around me better and obviously play with a quarterback who can really play the position,” Burress said. “It’s just about me picking my spots and seeing where I’m a good fit. You just look at a situation like Carolina. You can’t come across a better situation than that.”Burress returned to the NFL last season after missing two seasons thanks to a prison sentence for accidentally shooting himself in the leg at a nightclub.Burress, 34, tied for the Jets’ lead with eight touchdown receptions but never really clicked with quarterback Mark Sanchez, finishing with 612 yards and 45 receptions.
198476ers52-30+2.4Lost 1st Round15871572-15 ELO RATING IN PLAYOFFS 2012Heat46-20+6.4Won Title16131712+99 2009Cavaliers66-16+8.9Lost Conf. Finals17251742+17 1979Bullets54-28+4.8Lost Finals15811554-27 Average* * Average excludes 2017 Cavaliers.Source: Basketball-reference.com 1982Celtics63-19+6.4Lost Conf. Finals16861703+17 2007Cavaliers50-32+3.9Lost Finals15981621+23 1999Bulls13-37-8.6Missed playoffs1355—— 2000Spurs53-29+5.9Lost 1st Round16371625-12 2001Lakers56-26+3.7Won Title16471779+132 2011Heat58-24+7.5Lost Finals16721702+30 2007Heat44-38-1.2Lost 1st Round15051479-26 2006Spurs63-19+6.7Lost 2nd Round16851675-10 1988Lakers62-20+4.8Won Title16431662+19 2010Lakers57-25+4.8Won Title16131695+82 2003Lakers50-32+2.7Lost 2nd Round16551651-4 1981Lakers54-28+3.3Lost 1st Round16101595-15 2013Heat66-16+7.0Won Title17571754-3 Forget looking at defending champions, though. The more important variable, as far as sports bettors are probably concerned, is LeBron. Between his experience, his toughness, and his ability to thrive in crunch-time situations, he has a game well tailored to the playoffs. And that shows up in the data: 2014Heat54-28+4.8Lost Finals15811604+23 1993Bulls57-25+6.2Won Title16791726+47 1986Lakers62-20+6.8Lost Conf. Finals16521647-5 How defending NBA champions fared the next season 2002Lakers58-24+7.2Won Title16761738+62 1996Rockets48-34+1.6Lost 2nd Round14851497+12 2005Pistons54-28+3.3Lost NBA Finals16131689+76 1994Bulls55-27+2.9Lost 2nd Round15751607+32 16441658+15 1990Pistons59-23+5.4Won Title16661716+50 1989Lakers57-25+6.4Lost NBA Finals16371677+40 2012Mavericks36-30+1.8Lost 1st Round15471525-22 2016Warriors73-9+10.4Lost NBA Finals17881756-32 2013Heat66-16+7.9Won Title17571754-3 1987Celtics59-23+6.6Lost NBA Finals16761659-17 2004Spurs57-25+7.5Lost 2nd Round17341719-15 1997Bulls69-13+10.7Won Title17661802+36 2017Cavaliers51-29+3.2TBD1566TBDTBD 1998Bulls62-20+7.2Won Title17281785+57 ELO RATING IN PLAYOFFS LeBron James’s teams usually find a higher gear in the playoffs 1985Celtics63-19+6.5Lost NBA Finals16681685+17 Average* 2008Spurs56-26+5.1Lost Conf. Finals16621678+16 1991Pistons50-32+3.1Lost Conf. Finals15521535-17 * Average excludes 1999 Bulls and 2017 CavaliersSource: Basketball-reference.com 2017Cavaliers51-29+3.4TBD1566TBDTBD 1983Lakers58-24+5.1Lost Finals16051606+1 2015Spurs55-27+6.3Lost 1st Round17331721-12 1980SuperSonics56-26+4.2Lost 2nd Round16391614-25 2016Cavaliers57-25+6.0Won Title16421759+117 1992Bulls67-15+10.1Won Title17691762-7 2011Lakers57-25+6.0Lost 2nd Round16591624-35 2006Cavaliers50-32+2.2Lost 2nd Round15621564+2 1995Rockets47-35+2.3Won Title15311665+134 The Cleveland Cavaliers haven’t been any good lately. And I don’t just mean their loss Sunday against the Atlanta Hawks, in which they became only the third team in NBA history to blow a 26-point fourth-quarter lead. They’re 12-13 since the All-Star break. They have one of the NBA’s worst defenses, having allowed 107.9 points per 100 possessions — in the same territory as the Orlando Magic and the New York Knicks. They haven’t won a road game against a Western Conference playoff team all season. But handicappers think LeBron James and company have a pretty good chance of winning their second-straight NBA title anyway.Their view depends on their belief in the existence of Playoff LeBron, a superhero that transcends his already-formidable regular season form to carry his team to ever-greater heights. The good news for Cavs’ fans is that Playoff LeBron exists. He just might not be mighty enough to drag this team to a title.On the basis of their regular-season record and point differential, this season’s Cavs have been in the same general vicinity as teams such as the Boston Celtics, Toronto Raptors and Utah Jazz. Those teams are variously 30-to-1 to 100-to-1 longshots to win the title, according to Vegas bookmakers. But the Cavs are nonetheless the second-favorite team to win the championship, with a 20 to 25 percent chance according to bookmakers.Computer systems disagree. All of them have the Warriors as odds-on favorites to win the title, with the San Antonio Spurs as the next-best bet, and the Cavs as part of an undistinguished mass of teams beneath them. ESPN’s BPI puts Cleveland’s chances at just 4 percent. Basketball-Reference’s playoff odds also have them at 4 percent. And FiveThirtyEight’s Elo-based ratings1In this article, I’m mostly ignoring the difference between Elo ratings and “CARM-Elo” ratings, which are Elo ratings adjusted for our pre-season CARMELO projections. Our forecasts are based on Carm-Elo ratings, but the Cavs’ Elo rating is 1566 and their Carm-Elo rating is 1562, so this makes little difference at this stage of the season., which heavily weight recent play, have them even lower at just 2 percent.Usually, Elo-type ratings mimic betting markets fairly well. We give the Warriors a 65 percent chance of winning the title, for instance, and the San Antonio Spurs an 11 percent chance — right in line with where markets have them. So what accounts for the huge difference on Cleveland?One explanation is that this is all just sort of irrational: the Cavs are a marquee team and bettors just can’t stomach the idea that they’re just the Raptors with better uniforms. But I’m not sure I totally buy that; NBA betting markets are usually fairly sharp.Instead, bettors expect the Cavs to find a higher gear in the postseason. This isn’t an idea they just came up with; it was already priced into their assessment of the Cavs before the year began. At the start of the NBA season, FiveThirtyEight’s projections forecast the Cavs to win 57 regular-season games. (They have 51 now, so they’ll finish with no more than 53 wins.) That forecast was almost the same as what Vegas gave them, which put their over-under at 56.5 wins. But we also gave the Cavs only an 11 percent chance of winning the title whereas Vegas put them at 5-to-2 against, or a 29 percent chance. In other words, handicappers and the computer models agree on “regular-season Cavs.” It’s just that Vegas thinks that “playoff Cavs” are different — and much better — whereas our Elo ratings make no such distinction.But is there good reason to think that Cleveland can turn it up a notch?It’s not hard to recall examples of defending champions that lollygagged their way through the regular season, only to show up as the best version of themselves in the playoffs. In 2000-01, Shaquille O’Neal, Kobe Bryant and the Los Angeles Lakers finished with a 56-26 record — better than the Cavs this year, but not by that much — before winning 15 of 16 playoff games and repeating as NBA champion. And Hakeem Olajuwon and the 1994-95 Houston Rockets finished at 47-35 before winning the title despite being the No. 6 seed. In his last season in Miami, James and the 2013-14 Miami Heat had an uninspired regular season, going 54-28. But they made the NBA finals before losing to San Antonio.I’ve done a bit of cherry-picking there, however. Overall, it’s not clear if defending champs overperform by much in the playoffs. In the table below, I’ve looked at every defending NBA champion since the ABA-NBA merger in 1976-77. If the “higher gear” theory is correct, then they should systematically beat Elo’s expectations in the postseason, in which case their Elo ratings will rise over the course of the playoffs. On average, however, these teams’ Elo ratings increased only from 1644 to 1658 during the playoffs.2This average excludes the 1998-99 Chicago Bulls, the only NBA champion since the merger to miss the playoffs. So there’s a little something there, but in Elo terms, that’s pretty minor — not much more than a rounding error.3An Elo rating of 1644 is equivalent to 55.5 regular season wins in an 82-game season, while an Elo rating of 1658 is equivalent to 56.7 wins. 2010Cavaliers61-21+6.5Lost 2nd Round17011646-55 YEARTEAMRECORDPOINT DIFF.PLAYOFFSSTARTENDDIFF. 2008Cavaliers45-37-0.3Lost 2nd Round15061562+56 2015Cavaliers53-29+4.4Lost Finals16311692+61 2014Heat54-28+4.2Lost Finals15811604+23 2009Celtics62-20+7.4Lost 2nd Round16931653-40 16351669+34 1978Trail Blazers58-24+5.9Lost 1st Round15581551-7 YEARTEAMRECORDPOINT DIFF.PLAYOFFSSTARTENDDIFF. James’ teams have made the playoffs 11 times prior to this season. And they’ve played really well, both in absolute terms and relative to their regular-season performance. James and the Cavs did have a disastrous postseason in 2010 — when, as the No. 1 overall seed, they lost to the Celtics in the second round — but that’s pretty much the only exception. On average, they’ve gained 34 Elo points from the start of the playoffs to the end. And over James’s past six postseasons, they’ve outperformed their regular-season ending Elo rating by an average of 55 points.So let’s say that Elo has the Cavs’ underrated by somewhere in the neighborhood of 50 points. Call that a “LeBron clutch factor” or whatever else you like. I asked my colleague Jay Boice to add 50 Elo points to the Cavs’ Elo rating and rerun our playoff simulations. Their championship odds rose … but only to 6 percent.Instead, you have to add about 150 points of Elo rating to get the Cavs’ odds in the same vicinity as Vegas has them.4With a 150 Elo-point boost, the Cavaliers’ NBA title probability would be 21 percent. That’s a lot. Elo sees the Cavs’ current level of performance as equivalent to a 48-34 regular-season record. Add 150 Elo points to that total, and they’d project to a 62-20 regular season record. That’s a 14-win gain — about what you’d get from adding someone like Kawhi Leonard or Anthony Davis to the roster.Have no doubt: I’d love to plunk some money down on the Cavs at the odds our forecast and the other computer models give them. Playoff basketball is a pretty different specimen from regular-season basketball, and our model isn’t doing anything to account for that. This is something for us to examine for future iterations of the model, even if the Cavs get bounced in the first round.But I also wonder if the bookies aren’t going too far in the other direction. There are plenty of defending champions — and James-led teams — that underwhelmed in the regular season before going on to win a title or at least reach the finals. But few of them underperformed as much as the Cavs have. They also tended to benefit from down periods in the league, as the 1994-95 Rockets and 2000-01 Lakers did. This year, the Cavs will have to get past the Warriors, who might be even better than last year’s 73-9 version5The Warriors’ point differential is better last year’s — and the fourth-best in NBA history — and they have Kevin Durant., or, failing that, probably the Spurs.Nor will the Cavs’ enter the postseason with much rest. Instead, as the East’s No. 1 overall seed has been up in the air between the Cavs and the Celtics, James has averaged 43 minutes per over the team’s last five games. Kyrie Irving has gotten only two days off since the All-Star break. Kevin Love has played heavy minutes despite missing time in February and March due to knee surgery.James has beaten expectations so many times in the playoffs that transcendent things are almost expected from him. If he leads the Cavaliers to another title this year it really might be his greatest accomplishment yet.
For years, the story of the Los Angeles Angels has been one of how far a singular talent — future Hall of Fame outfielder Mike Trout — could drag a group of otherwise unspectacular players. The answer was usually “not much further than 85 wins” — and even fewer in recent seasons. This year’s Angels are turning that narrative on its head, however, and it’s not just thanks to the emergence of two-way rookie Shohei Ohtani. Going into its game against the Boston Red Sox on Tuesday night, L.A. has begun the season as a case study in how successful a team can be when a player of Trout’s skill is finally surrounded by a handful of worthy teammates.Trout, of course, is just as good as ever. If it seems like we write about his otherworldly consistency every season, that’s because every season he somehow finds a way to keep adding to his legend. This year, Trout is once again the betting favorite to win the American League’s MVP award, and he leads the AL in Baseball-Reference.com’s version of wins above replacement (WAR).1According to an average of Baseball-Reference and FanGraphs’ systems, which I prefer to use because it evens out the quirks of each method, Trout ranks third behind Oakland’s Matt Chapman and Didi Gregorius of the Yankees. But honestly, does anyone really believe either will be able to stave off Trout for long? Once he gets that .244 batting average on balls in play straightened out (his career BABIP is .353), this will end up looking like a vintage Trout season — which is to say, the kind of year you’d expect to see out of the greatest player ever.(Yes, here comes the GOAT talk again: With 146 games remaining this year, Trout needs just 7.6 more WAR to catch Ty Cobb as history’s greatest position player through age 26. Over the previous three seasons,2One of which included significant time lost to injury. Trout has averaged 7.9 WAR per 146 team games, so he has a good chance to be back in his familiar perch before season’s end.)The Angels have been spoiled by Trout for so long that it’s easy to take his greatness for granted. That’s especially true because he plays a sport in which the best player on the planet can really only improve a team’s record by something like seven or eight wins over league average in a 162-game season. LeBron James, by contrast, added about 20 wins above average to the Cleveland Cavaliers this season3Using Basketball-Reference.com’s Box Plus/Minus metric. James had a +9.6 BPM this season while playing 76.7 percent of Cleveland’s available minutes. According to this BPM explainer, that means James was worth 7.4 points to the Cavaliers’ scoring differential per 100 possessions, which translates to 19.9 wins above average after multiplying by 2.7 to convert between scoring margin and wins. (Incidentally, the fact that Cleveland “only” won 50 games speaks volumes about the quality of King James’s teammates this year.) in roughly half as many games. While elite basketball players can carry mediocre teammates far, baseball’s superstars need more help. This year, Trout is finally getting that help.Last season, shortstop Andrelton Simmons was the only one of Trout’s teammates to play at a substantially above-average clip, according to WAR.4Using the same average between FanGraphs and Baseball-Reference’s numbers, Simmons produced 3.9 more wins than an average AL position player. Behind him among non-Trout Angels were pitchers Yusmeiro Petit and Blake Parker at 0.9 wins above average apiece. But this season, Trout has 11 teammates on pace for 2.0 or more wins above average and seven on track for at least 3.0: Ohtani, Simmons, Justin Upton, Tyler Skaggs, Rene Rivera, Jefry Marte and Zack Cozart. Even the legendary Albert Pujols, who was infamously the worst player in baseball last season according to WAR, has been much better this year, tracking for a shade over 2.0 WAR per 162 games.The result has been the best team in baseball by WAR in the early going and easily the best performance by a set of Trout teammates in any season of his career: Some of these early standouts are more likely to stay hot than others. In large part because of his outstanding work with the glove, Simmons has been one of the best (and most underrated) shortstops in the game for years. Upton5Who didn’t join the Angels until a trade at least year’s deadline. and Cozart were very good last season as well. Ohtani’s stellar raw skills and versatility mean he’ll probably keep contributing throughout his rookie season. And Pujols’s hideous 2017 numbers might have been an aberration after all. Other players, such as Skaggs, Marte and Rivera, might fall off pace, given their track records. But even if a few of Trout’s teammates do come back to earth, this is still looking like the strongest Angels team Trout has had to work with in a while.Fittingly, no team has tacked more points onto its FiveThirtyEight power rating since preseason than L.A. has so far. It’s not like we’re rating the Angels ahead of the defending world champion Astros in the AL West or anything yet — Houston is still a solid 53 percent favorite to win the division. But Los Angeles should at least pose a fight. And that’s more than the challenger in this division has been able to say in a while: Over the past decade, no division has been decided by more games on average than the AL West, whose average winner was clear of the runner-up by 9.8 games per season. With Trout playing like, well, Trout, and his teammates giving him the support he deserves, the Angels look like a team to be reckoned with again.Check out our latest MLB predictions.
The Philadelphia 76ers are going to make the playoffs — not that their best players know what that feels like. Teams with their level of inexperience don’t usually push past the first round. Can the Sixers break the mold?
Before Wednesday’s trade headlined by Paul Goldschmidt, the Arizona Diamondbacks and St. Louis Cardinals were relatively even on paper. But today, the two clubs inhabit completely different neighborhoods.Arizona and St. Louis ended last year separated by 25 points in Elo rating, and the teams entered Wednesday just two games apart in FanGraphs’ projected standings for 2019. With the trade of the six-time All-Star, the clubs have seemingly chosen different paths. The Diamondbacks appear ready to join the Seattle Mariners as teams that contended in 2018, fell short and have elected to become less competitive to restore their depleted talent bases. The Cardinals add a star talent with the hope that they can close the gap in the National League Central and return the club to the postseason after a three-year absence.The Diamondbacks are in a division with the powerful Los Angeles Dodgers, who are loaded with cash and talent and are heavy favorites in the NL West. Arizona already lost one key free-agent pitcher in Patrick Corbin, who agreed Tuesday to a deal with the Washington Nationals, and free-agent center fielder A.J. Pollock also figures to land elsewhere. The club has also expressed interest in trading ace Zack Greinke, whose contract accounted for 25.8 percent of the club’s opening day payroll this past season — the second-highest share in the majors. It’s a reminder that such contracts can hamstring teams’ abilities to build complete, competitive rosters.Conversely, the Cardinals do not have a clear super team in their way in the NL Central. The Cubs might have limited ability to improve this offseason, but the FanGraphs’ forecast has the Brewers regressing in 2019. The Cardinals entered Wednesday projected for four fewer wins than the Cubs, three more wins than the Pirates and six more wins than the Brewers. After the trade, the FanGraphs projection had the Cardinals picking up three wins to be just one game behind the Cubs and nine games better than the Brewers. (The Diamondbacks fell from 82 to 80 wins.) The Cardinals have been stuck in the standings purgatory — winning 88, 83 and 86 games the past three years — where no club wants to reside, but they could break that streak this year.The Diamondbacks went for it last year on the heels of a 93-win season and in the final year of control over Corbin and Pollock. St. Louis is now in a similar situation, as contributors like Marcell Ozuna, Miles Mikolas and Michael Wacha are free agents after 2019. Goldschmidt is under control for just one season before entering free agency. For the Cardinals, this is a win-now move.And what St. Louis received in the deal is not only one of the game’s best hitters but also one of its most consistent.In wins above replacement,1Using FanGraphs’ metric. Goldschmidt finished the past three seasons at 5.1, 5.2 and 5.0. He’s been worth at least 4.3 WAR every season since his first full year in 2012, when he finished at 2.8. Goldschmidt’s career slash line is .297/.398/.532. His slash line this past season? .290/.389/.533. He’s played in at least 155 games in five of the past six years.Goldschmidt, 31, is still near his physical prime and offers consistent star power for a club sorely lacking it. St. Louis thought it was landing a star in Ozuna last winter, but he had a mildly disappointing season. Since 2016, the only Cardinals to deliver seasons of 4 WAR or better were Matt Carpenter (5.0) and Mikolas (4.3) this past season and Tommy Pham, who was traded to Tampa Bay last season, in 2017 (6.1). Goldschmidt’s 4.3 projected WAR is a big upgrade over the Cardinals’ weakest projected starting infielder, Jedd Gyorko (1.7 WAR) — who could be supplanted in the lineup by Carpenter moving from first to third. And Goldschmidt may not even be the Cards’ final step: Ownership hasn’t ruled out a pursuit of Bryce Harper.While there is not a young star in the trade package, Goldschmidt didn’t come cheap. Some executives liked the return for Arizona, which included young major leaguers in pitcher Luke Weaver and catcher Carson Kelly, infield prospect Andy Young and the Cardinals’ Compensation Round B selection in the 2019 draft. The deal gives the Diamondbacks youth and a number of controllable years.The Diamondbacks had the fifth-oldest groups of batters (at an average of 29.2 years old)2Weighted by games played. and pitchers (29.6) last year. According to FanGraphs, Arizona entered the offseason with the game’s 26th-ranked farm system. Teams prize young, cheap, controllable talent — and now more than ever before, they are willing to endure deep, painful rebuilds to accumulate high draft picks and signing bonus pool space. The Astros and Cubs created a model to get to super-team status that other teams are following. Those clubs took rebuilding to extreme degrees, stringing together multiple 95-plus-loss seasons, but those paths resulted in World Series titles.The Diamondbacks consider themselves to be retooling rather than entering a deep rebuild, though that might be an optimistic assessment: Arizona third baseman Jake Lamb, outfielder David Peralta and starting pitchers Robbie Ray and Taijuan Walker are all eligible to become free agents within the next two to three years.Kelly and Weaver immediately fill needs on the major league roster. They are not prospects that are years away from the majors, though they also lack star-level upside.3The Diamondbacks were in the market for a catcher after free agent Jeff Mathis signed with the Texas Rangers. “There are decisions that you want to do and there are decisions you feel like you have to do,” Diamondbacks GM Mike Hazen said of Wednesday’s trade.More than ever, teams seem comfortable entering retooling periods, but not every rebuild is a successful project. For the Cardinals, perhaps they’ll have to consider such a path down the road. As for 2019, they’re going for it.
Model CreatorsNate Silver, Jay Boice and Neil Paine ReferencesBasketball-Reference.com / Real Plus-MinusElo ratings / Monte Carlo simulations / Box Plus/Minus / Simple Projection System The DetailsFiveThirtyEight’s NBA predictions have gone through quite an evolution over the years.Our first iteration simply relied on Elo ratings, the same old standby rating system we’ve used for college and pro football, college basketball, baseball, soccer, Formula One racing and probably some other sports I’m forgetting. Basic Elo is generally useful — and we still track it for teams going back throughout history — but it only knows who won each game, the margin of victory and where the game was played. So if a player is injured or traded — or resting, as is increasingly the case in the NBA — Elo wouldn’t be able to pick up on that when predicting games or know how to account for that in a team’s ratings going forward. In fact, even if a team simply made a big offseason splash (such as signing LeBron James or Kevin Durant), Elo would take a long time to figure that out, since it must infer a change in team talent from an uptick in on-court performance.To try to address that shortcoming, in 2015 we introduced a system we called “CARM-Elo.” This still used the Elo framework to handle game results, but it also used our CARMELO player projections to incorporate offseason transactions into the initial ratings for a given season. In a league like the NBA, where championships now feel like they’re won as much over the summer as during the season itself, this was an improvement. But it still had some real problems knowing which teams were actually in trouble heading into the playoffs and which ones were simply conserving energy for the games that matter. Since a team’s underlying talent is sometimes belied by its regular-season record — particularly in the case of a superteam — an Elo-based approach to updating ratings on a game-to-game basis can introduce more problems than it actually solves.Moving beyond EloOne attempt to salvage CARM-Elo was to apply a playoff experience adjustment for each team, acknowledging the NBA’s tendency for veteran-laden squads to play better in the postseason than we’d expect from their regular-season stats alone. This also helped some, but CARM-Elo still had problems with mega-talented clubs (such as the 2017-18 Golden State Warriors) that take their foot off the gas pedal late in the NBA’s long regular season. It was clear our prediction system needed a major overhaul, one that involved moving away from Elo almost completely.As we hinted at in our preview post for the 2018-19 season, we made some big changes to the way we predict the league that year. Chief among them is that our team ratings are now entirely based on our CARMELO player projections. Specifically, each team is judged according to the current level of talent on its roster and how much that talent is expected to play going forward. Here’s how each of those components work:Talent ratingsAt their core, our CARMELO projections forecast a player’s future by looking to the past, finding the most similar historical comparables and using their careers as a template for how a current player might fare over the rest of his playing days. After running a player through the similarity algorithm, CARMELO spits out offensive and defensive ratings for his next handful of seasons, which represent his expected influence on team efficiency (per 100 possessions) while he’s on the court. You can think of these as being similar to ESPN’s Real Plus-Minus or other adjusted plus-minus-style ratings.The player ratings are currently based on a blend between Real Plus-Minus (RPM), Box Plus/Minus (BPM) and, on defense, our new DRAYMOND metric. In our mind, each of these metrics represents a different facet of measuring player performance: BPM to capture and weight the player’s contributions in the traditional box score; RPM to measure the effect he has on his team’s efficiency when he is on the court; DRAYMOND to capture a critical but underutilized aspect of defense, which is to reduce opponent shooting percentages by contesting and otherwise disrupting shots. A blend of all applicable metrics appears in the CARMELO individual pages under the player’s offensive rating (using BPM and RPM) and defensive rating (using BPM, RPM and DRAYMOND).These blended ratings provide a prior for each player as he heads into the current season. But they must also be updated in-season based on a player’s performance level as the year goes on. To do that, we have two methods (for both offense and defense) depending on the data available:Using Real Plus-Minus and Box Plus/Minus. Ideally, both metrics will be published during a season, allowing us to use a blend (⅔ weight for RPM; ⅓ for BPM) on each side of the ball to update our prior ratings. When that happens, we assign a weight to the prior that is relative to 1 minute of current-season performance. On offense, that weight is calculated with a constant term of 416 minutes, plus 0.3 times a player’s minutes from the season before, plus 0.15 times his minutes from two seasons before, plus 0.05 times his minutes from three seasons before. That number is multiplied by his CARMELO preseason offensive rating and added to the product of his current-season minutes and current-season offensive plus/minus blend, and divided by the sum of current-season minutes and the prior weight to get an updated offensive rating. (The rating for players with 0 current-season minutes played is, by definition, the prior.) On defense, the weight has a constant of 60 minutes, plus 0.3 times a player’s minutes from the season before, plus 0.15 times his minutes from two seasons before, plus 0.05 times his minutes from three seasons before. This weight is combined with current-season performance in the same manner as on offense.Using Box Plus/Minus only. At a certain stage of each season, ESPN will not have released RPM data for the current season yet. During that time, we must update the in-season ratings using only BPM, which is usually available since the very start of the season via Basketball-Reference.com. Just like with our blended number from above, we assign a weight to the prior that is relative to 1 minute of current-season performance — but we must use different weights because BPM is not quite as reliable an indicator of player performance as RPM (or our RPM-BPM blend). On offense, the weight is calculated with a constant term of 703 minutes, plus 0.27 times a player’s minutes from the season before, plus 0.13 times his minutes from two seasons before, plus 0.04 times his minutes from three seasons before. That number is multiplied by his CARMELO preseason offensive rating and added to the product of his current-season minutes and current-season offensive plus/minus blend, and divided by the sum of the current-season minutes and the prior weight to get an updated offensive rating.On defense, the weight has a constant of 242 minutes, plus 0.48 times a player’s minutes from the season before, plus 0.24 times his minutes from two seasons before, plus 0.08 times his minutes from three seasons before. This weight is combined with current-season performance in the same manner as on offense.Regardless of the version being used, these talent ratings will update every day throughout the regular season and playoffs, gradually changing based on how a player performs during the season.Overnight updatesBecause our data sources for player ratings (ESPN and Basketball-Reference.com) don’t update individual statistics immediately after the end of every game, we added a function to preliminarily estimate the changes to a team’s rating as soon as a game ends. For each player in our database, we adjust his offensive and defensive ratings up or down very slightly after each game based on his team’s margin of victory relative to CARMELO’s expectation going into the game. These numbers add up at the team level to reflect how we predict that a team’s ratings will change in the wake of a given result.The advantage of this is that we can provide an instant update to the model as soon as a game ends. However, since these estimates are stopgaps, they will be changed to the RPM/BPM-based ratings from above when the data from those sources updates. After any given game, these differences should be small and generally barely noticeable. But we think this change will be particularly worthwhile in the playoffs, when team odds can shift dramatically based on a single game’s result.Playing-time projectionsNow that we have constantly updating player ratings, we also need a way to combine them at the team level based on how much court time each player is getting in the team’s rotation.For CARM-Elo’s preseason ratings, we used to accomplish this by manually estimating how many minutes each player would get at each position. Needless to say, this is a lot more work to do in-season (and it requires a lot of arbitrary guesswork). So as part of our move toward algorithmizing our predictions in a more granular way, we developed a program that turns simple inputs into a matrix of team minutes-per-game estimates, broken down by position.This system requires only a rank-ordered list of players on a given team by playing-time preference (the default order is sorted by expected rest-of-season wins above replacement), a list of eligible positions a player is allowed to play (the system will assign minutes at every player’s “primary” position or positions first, before cycling back through and giving minutes at any “secondary” positions when necessary to fill out the roster) and some minutes constraints based largely on CARMELO’s updating minutes-per-game projections.For that last part, we have developed an in-season playing-time projection similar to the one we use to update our individual offensive and defensive ratings. For each player, CARMELO will project a preseason MPG estimate based on his own history and the record of his similar comparables. We then adjust that during the season by applying a weight of 12.6 games to the preseason MPG projection, added to his current-season minutes and divided by 12.6 plus his current-season games played. (Interestingly, this implies that the amount of weight the MPG prior receives is the same regardless of whether the player is a fresh-faced rookie or a grizzled veteran.)Those minutes are used as the default for our program, which then automatically creates a team’s depth chart and assigns minutes by position according to its sorting algorithm. The defaults, however, can and will be tweaked by our staffers to help the program generate more accurate rosters. For instance, we can mark certain games in which a player is injured, resting, suspended or otherwise unavailable, which will tell the program to ignore that player in the team’s initial rank-ordered list of players before allocating minutes to everyone else. (We also have a method of penalizing a player’s talent ratings if he is forced to play significantly more MPG than his updated CARMELO projection recommends.) New for 2020, there is even a “load management” setting that allows certain stars to be listed under a program of reduced minutes during the regular season.Through this system, we will be able to account for most injuries, trades and other player movement throughout the season on a game-by-game basis.Because of the differences between a team’s talent at full strength and after accounting for injuries, we now list two separate CARMELO ratings on our interactive page: “Current CARMELO” and “Full-Strength CARMELO.” Current is what we’re using for the team’s next game and includes all injuries or rest days in effect at the moment. Full-strength is the team’s rating when all of its key players are in the lineup, even including those who have been ruled out for the season. This will help us keep tabs on which teams are putting out their best group right now, and which ones have room to improve at a later date (i.e., the playoffs) or otherwise are more talented than their current lineup gives them credit for.Game predictionsAs a consequence of the way we can generate separate depth charts for every team on a per-game basis, we can calculate separate CARMELO ratings for the teams in a matchup depending on who is available to play.For a given lineup, we combine individual players’ talent ratings into a team rating on both sides of the ball by taking the team’s average offensive and defensive rating (weighted by each player’s expected minutes) multiplied by 5 to account for five players being on the court at all times. Those numbers are then combined into a generic expected “winning percentage” via the Pythagorean expectation:Winning Percentage=(108+Team Offensive Rating)14(108+Team Offensive Rating)14+(108−Team Defensive Rating)14Winning Percentage=(108+Team Offensive Rating)14(108+Team Offensive Rating)14+(108−Team Defensive Rating)14That number is then converted into its Elo rating equivalent via:CARMELO Rating=1504.6−450×log10((1/Winning Percentage)−1)CARMELO Rating=1504.6−450×log10((1/Winning Percentage)−1)From there, we predict a single game’s outcome the same way we did when CARM-Elo was in effect. That means we not only account for each team’s inherent talent level, but we also make adjustments for home-court advantage (the home team gets a boost of about 92 CARMELO rating points), fatigue (teams that played the previous day are given a penalty of 46 CARMELO points), travel (teams are penalized based on the distance they travel from their previous game) and altitude (teams that play at higher altitudes are given an extra bonus when they play at home, on top of the standard home-court advantage). A team’s odds of winning a given game, then, are calculated via:Win Probability=1/(10−(CARMELO Differential+Bonus Differential)/400+1)Win Probability=1/(10−(CARMELO Differential+Bonus Differential)/400+1)Where CARMELO Differential is the team’s talent rating minus the opponent’s, and the bonus differential is just the difference in the various extra adjustments detailed above.Season simulations and playoff adjustmentsArmed with a list of injuries and other transactions for the entire league, our program can spit out separate CARMELO ratings for every single game on a team’s schedule. For instance, if we know a player won’t be available until midseason, the depth-chart sorting algorithm won’t allow him to be included on a team’s roster — and therefore in the team’s CARMELO ratings — until his estimated return date.Those game-by-game CARMELO ratings are then used to simulate out the rest of the season 50,000 times, Monte Carlo-style. The results of those simulations — including how often a team makes the playoffs and wins the NBA title — are listed in our NBA Predictions interactive when it is set to “CARMELO” mode.It’s important to note that these simulations still run “hot,” like our other Elo-based simulations do. This means that after a simulated game, a team’s CARMELO rating is adjusted upward or downward based on the simulated result, which is then used to inform the next simulated game, and so forth until the end of the simulated season. This helps us account for the inherent uncertainty around a team’s CARMELO rating, though the future “hot” ratings are also adjusted up or down based on our knowledge of players returning from injury or being added to the list of unavailable players.For playoff games, we make a few special changes to the team CARMELO process explained above. For one thing, teams play their best players more often in the playoffs, so our depth-chart algorithm has leeway to bump up a player’s MPG in the postseason if he usually logs a lot of minutes and/or has a good talent rating. This year, CARMELO outputs a separate recommended-minutes-per-game projection for both the regular season and the playoffs.We also have added a feature whereby players with a demonstrated history of playing better (or worse) in the playoffs will get a boost (or penalty) to their offensive and defensive talent ratings in the postseason. For most players, these adjustments are minimal at most, but certain important players — such as LeBron James — will be projected to perform better on a per-possession rate in the playoffs than the regular season. (Truly, he will be in “playoff mode.”)And we continue to give a team an extra bonus for having a roster with a lot of playoff experience. We calculate a team’s playoff experience by averaging the number of prior career playoff minutes played for each player on its roster, weighted by the number of minutes the player played for the team in the regular season. For every playoff game, this boost is added to the list of bonuses teams get for home court, travel and so forth, and it is used in our simulations when playing out the postseason.The complete history of the NBAIf you preferred our old Elo system without any of the fancy bells and whistles detailed above, you can still access it using the NBA Predictions interactive by toggling its setting to the “pure Elo” forecast.This method still has the normal game-level adjustment for home-court advantage, but it doesn’t account for travel, rest or altitude; it doesn’t use a playoff-experience bonus; and it has no knowledge of a team’s roster — it only knows game results. It also doesn’t account for any offseason transactions; instead, it reverts every team ¼ of the way toward a mean Elo rating of 1505 at the start of every season. We use a K-factor of 20 for our NBA Elo ratings, which is fairly quick to pick up on small changes in team performance.You can also still track a team’s Elo rating in our Complete History of the NBA interactive, which shows the ebbs and flows of its performance over time. This number won’t be adjusted for roster changes, but it should remain a nice way to visualize a team’s trajectory throughout its history. Version History4.0 CARMELO updated with the DRAYMOND metric, a playoff adjustment to player ratings and the ability to account for load management. Pure Elo ratings now use a K-factor of 20 in both the regular season and the playoffs.3.1 Estimated overnight ratings added as a stopgap between game results and data updates.3.0 CARMELO is introduced to replace CARM-Elo. Pure Elo ratings are adjusted to have variable K-factors depending on the stage of the season being predicted.2.1 CARM-Elo is modified to include a playoff experience adjustment.2.0 CARM-Elo ratings are introduced. Seasonal mean-reversion for pure Elo is set to 1505, not 1500.1.0 Pure Elo ratings are introduced for teams going back to 1946-47. Related ArticlesThe Complete History Of The NBAHow We Calculate NBA Elo RatingsThe Best NBA Teams Of All Time, According To EloHow Our 2015-16 NBA Predictions WorkWhy The Warriors And Cavs Are Still Big FavoritesFrom The Warriors To The Knicks, How We’re Predicting The 2018-19 NBA
Torrance Gibson checking into the team hotel before 2016 fall camp. Credit: Sheridan Hendrix | Lantern PhotographerJust a week after OSU coach Urban Meyer announced redshirt freshman wide receiver Torrance Gibson had practiced for the first time in 10 days, Gibson has been suspended for the entire 2016 season for a violation of team rules.Gibson took a redshirt last season after injuring his ankle. It was rumored another key factor in the decision to redshirt the former No. 6 athlete in the class of 2015 was problems with Gibson in the classroom.Gibson played in the 2016 spring game for the Gray team scoring twice in his first action in Ohio Stadium.
The Buckeyes got a very important victory on Saturday: 31-13 over Wisconsin. The win was anything but conventional, but it did solidify the Buckeyes as the leaders of the Big Ten.“It was a little bit different ball game than I think any of us anticipated,” coach Jim Tressel said in his weekly press conference. “We knew Wisconsin was a heck of a football team, and we knew we were going to have our hands full.”Once again, the defense led OSU to a comfortable victory by several scores, but offensively the Buckeyes have more questions than answers.OSU (5-1, 3-0) now prepares for the Purdue Boilermakers (1-5, 0-2) on Saturday.Defense comes up big yet againAll season, the Buckeyes’ defense has made plays when necessary. Saturday wasn’t any different.Senior captain Kurt Coleman returned from a one-game suspension and wasted little time returning to form. His 89-yard interception return stopped a driving Badger team and shifted momentum into the Buckeyes’ favor for the remainder of the game. His 14 tackles were also a career high.“The funny thing about it, I was joking with my teammates and they were telling me that because I missed a game I needed to have double the production, and it just so happened that I happened to have double the production. It was a great feeling to come back and help this team, especially in that kind of fashion. I was just happy to be out there playing with my teammates again.”Tressel said the Badgers played up to his expectations, but the Buckeyes “came up with some plays that are game changers.”“Obviously, Kurt Coleman’s play and the pressure that was put on the quarterback, his being where he was supposed to be, and then I thought the execution on the interception return was outstanding,” Tressel said. “You always say when an interception is thrown, if you can block the intended receiver and you can block the quarterback and then get everyone else running down the sideline, you have a chance, and they did that just as you would hope they would do.”OSU got another interception for a touchdown in the second half. With the game still in doubt, junior safety Jermale Hines put the Buckeyes up two touchdowns with a 32-yard interception return.The defense will look to control Purdue’s high-powered offense on Saturday. The Buckeyes have held all but one opponent to under 20 points, including back-to-back shutouts of Toledo and Illinois.Pryor takes a step backAfter having nine touchdowns in the last three games, quarterback Terrelle Pryor saw his production drop significantly against the Badgers.The sophomore completed 5 of 13 passes for only 87 yards with one touchdown and an interception. His one bright spot for the afternoon came right before halftime, when he led the Buckeyes 88 yards for the go-ahead score. A run of 27 yards and a touchdown pass for 32 yards to DeVier Posey were the highlights of the drive.The offense struggled to find a rhythm because of three touchdown by the defensive and special teams. However, OSU would have liked to see more consistency from its quarterback.The problems on offense do not begin and end with one guy, Tressel stressed. The lack of production was an offensive problem, not just a problem with his quarterback.“I think the thing you do with any of us is understand that when things go just right, I probably didn’t do it by myself, and when things didn’t go the way you’d like, it probably didn’t have everything to do with me,” Tressel said.No Boom, only ZoomThe depth at running back is quickly depleting for OSU. Daniel “Boom” Herron did not play against Indiana on Oct. 3 but returned against Wisconsin. Unfortunately for the Buckeyes, he re-injured his ankle and most likely will not be available against the Boilermakers.Freshman Jamaal Berry, who was a prized recruit for the Buckeyes this season, has also been injured. Whether he will make an impact this season remains to be seen.“I’m the eternal optimist. I keep waiting for Jamaal Berry to be healthy because when he’s been healthy, he’s been very, very good,” Tressel said. “Right now, it’s Brandon and Jordan.”Small finally makes large impactSenior wide receiver and returner Ray Small has spent his fair share of time in Tressel’s doghouse. The Glenville product, who many thought would be the next Ted Ginn Jr., has not lived up to expectations, and it was unsure whether he would even play this season.However, when he took a Badger kickoff 96 yards for a touchdown on Saturday, he put defenders and past troubles behind him. He said there is no better feeling than making an impact again.“It means a lot. I play the game as if it’s my last play, no matter if it’s practice or if it’s the game,” Small said. “This year I came in and take it a lot more serious.”Small said he has no regrets and would love to have another big game this weekend to show how far he has come.
Sophomores ruled the day Monday for Ohio State men’s basketball as the Big Ten Conference announced its all-conference selections and awards. Buckeyes’ sophomore forward Jared Sullinger was a first-team Big Ten selection by the coaches and media while sophomore guard Aaron Craft was named Defensive Player of the Year by the conference’s coaches. Sophomore forward Deshaun Thomas was named a second-team All-Big Ten selection by the coaches, and the media and coaches named senior guard William Buford to the second team. Sullinger, who averaged 17 points and nine rebounds per game, was a unanimous selection to the first team for a second consecutive year. Craft’s selection as the defensive player of the year is the first for OSU since 2007 and the fourth in the history of the program. Craft, who was also given a Big Ten Sportsmanship award, told the Big Ten Network that his defensive game benefitted from playing against his older brother as a child. “I couldn’t score on those guys,” Craft said. “I could frustrate them a lot on defense. I got in a couple fights with my brother and some of his friends. So it kind of started back then. Just growing up, (defense) is just something that I’ve enjoyed and I really like doing and understood that that is what is going to help the team the most.” Thomas and Buford both averaged 15 points and five rebounds per game. The Buckeyes (25-6, 13-5 Big Ten) jumped three spots in the latest Associated Press Top 25 poll to the No. 7 spot. OSU defeated Michigan State, 72-70, Sunday on a last-second 2-point shot by Buford. With the win, OSU clinched a three-way tie for the conference’s regular season title with the Spartans and Michigan. Though it conceded an outright regular season conference title with the loss to the Buckeyes, the MSU program came away with some major Big Ten hardware Monday as possible consolation. Spartans senior forward Draymond Green claimed the Big Ten’s Player of the Year award, as well as a unanimous first-team honor from the coaches and media. MSU coach Tom Izzo was a unanimous Coach of the Year honoree. Other award winners include Michigan freshman guard Trey Burke who split Freshman of the Year honors with Indiana forward Cody Zeller. OSU will begin postseason play on Friday at Bankers Life Fieldhouse in Indianapolis, Ind., as the No. 3 seed in the Big Ten Tournament. The Buckeyes will play the winner of Thursday’s Purdue-Nebraska opening-round game. OSU’s postseason will tip at about 9 p.m.
What might have been a hot streak for Ohio State’s women’s soccer team finally went cold in the first round of the NCAA Tournament. After capturing the Big Ten Tournament championship on Nov. 4, the No. 4-seeded Buckeyes (16-4-2) played 90 minutes of regulation and 20 minutes of overtime before losing to Oakland University on penalty kicks, 3-1, Saturday evening at Jesse Owens Memorial Stadium. The loss snapped a nine-game winning streak for OSU, a feat that tied the school record set back in 2004. Over the course of that span, the Buckeyes outscored opponents 25-5. While OSU had little trouble getting shots on the goal during Saturday’s contest, outnumbering Oakland 28-5, putting the ball in the back of the net proved a different story. “Soccer’s a cruel game and to outshoot a team 28-5 it’s a pretty tough one to swallow,” said OSU coach Lori Walker. “We have to credit Oakland for finding a way to win.” In the game’s shootout, Oakland managed to score on its first two kicks as redshirt senior goalkeeper Shannon Coley managed to thwart OSU’s first three attempts. After the Golden Grizzlies missed their next two shots, the Buckeyes made their fourth kick, keeping their hopes alive. Oakland, though, had other plans as it made its fifth and final shot to clinch the victory. For some members of the OSU squad that started this season’s campaign 0-2, it was their final time suiting up in the Scarlet and Gray. “I’m proud of every single girl on the roster and it’s a hell of a way to end a season. It’s unfortunate, the circumstance, but I can’t say that’s there’s any girl that I look and should have their head down,” said senior defender Lauren Granberg. Despite OSU’s 7-3 shot advantage in the first half, junior midfielder Julianne Boyle and the Golden Grizzlies drew first blood in the 41st minute and took the 1-0 lead. In the second half, the Buckeyes sent a barrage of 15 shots at Coley, who made four saves, but could not keep out one from OSU’s leading scorer. Senior forward Tiffany Cameron scored her 21st goal of the season in the 60th minute thanks to an assist from Granberg and sophomore midfielder Ellyn Gruber, knotting the score up at 1-1. Cameron led all members involved in the contest in shots on the goal with eight, three more than Oakland’s five tries. Cameron’s goal was the first score surrendered by Oakland since an Oct. 20 loss at Fort Wayne. With the loss the Buckeyes finish their 2012 season with a 16-4-2 mark and the program’s third Big Ten Tournament Championship. “To end our season on a tie and loss in penalty kicks is a pretty tough ending to a pretty fantastic season,” Walker said. Oakland will take on Texas A&M in the second round of the NCAA Tournament.