Tuesday, February 18, 2014

NBA Three-point Contest Simulation

All-star Weekend is over, but I worked overtime on a method to estimate a player's likelihood of winning the three-point contest. At first, I just wanted to find which factors correlated to contest success, but there's a frustrating lack of patterns within the contest results. The 2014 contest is over, but what can we learn from the past?

What translates into contest success

To set up the model, data is needed, obviously, but it's strange how difficult it is to find comprehensive results on every player and every round. The NBA's own website was missing the seasons 2010 and forward, and it had incorrect results for 2007. Thankfully, cross-checking with this thorough website from Spain and several news sources, every single round from the year 2000 to 2013 was included. Why not before 2000? For one, the shortened line of '95 to '97 means the data from the mid-90's isn't directly comparable, and the league has changed drastically with its three-point habits; comparing 1988 to 2010 is a fool's errand, basically.

After the data was captured, the model form was selected. Because it's count data (i.e. whole, positive numbers) and there's an upper limit on the score, a simple linear model can't be used. Instead a beta regression model is used where the dependent variable (what you're trying to predict) is the proportion of total points scored in a round. Beta regression is preferred when the scale is limited to 0 to 1, useful for rates and proportions. (Specifically, it's the logit link model used in the betareg function from R.) For example, the all-time record of 25 by the old rules would mean a proportion of 25/30 or 0.833. The functional form is: y = exp(beta*x)/( 1 + exp(beta*x) ).

The fun math aside, what variables predict the three-point contest scores? When I was collecting the data, I observed no patterns in the winners and the worst performers -- big name shooters like Bird and Peja have done well, but shooting specialists like Craig Hodges and Voshon Leonard excel too. Kevin Love surprisingly won a contest, so maybe star players have an advantage, but Curry and Durant were disappointing. Undeterred, I tested a variety of stats -- shooting percentages from the past three seasons (being careful to only look at the three-point percentage before the all-star break), three-point attempts per minute, usage rate (aka total shooting volume), height, returning champion, repeating participant, a dummy variable for the final round in case players "warm-up," year, and product combinations by multiplying, say, height by three-point attempts per minute in case there's an interaction effect.

The result? Unfortunately, only two variables were significant -- a weighted average of shooting percentage from the past three seasons(1), and a dummy variable for the final round. There is no correlation between other factors, as much as people like to state big guys have an advantage because they'll somehow get less tired heaving the shots due to their size. Thankfully, there was one interesting result with the shooting percentages -- the previous season is as important as the current one. One would think the more recent season is more relevant, and this is even more surprising because the previous season is full and has more attempts, but there's a selection bias for the contest: the NBA chooses players based on their shooting percentages and number of attempts from the current season. Thus, players who are having an anomalous half-season are more likely to be chosen, and their "true" three-point talent level is generally lower than what their pre-all star break percentage suggests.

(1) (3*3P_year0+3*3P_year1+2*3P_year2)/(3*3PA_year0+3*3PA_year1+2*3PA_year2)

From 2006 to 2013, the average difference in three-point percentage between the pre- and post- all-star break was 3.3% (among players with at least 40 attempts after the break) -- meaning, a player is likely a significantly worse shooter the rest of the season. However, there's a wrench in the selection bias: champions are invited back unless they're injured. This is probably the best comparison test since they are not selected because of their pre-break percentage. And among returning champions, the average difference is ... 0.02. Clearly, there's selection bias when only looking at a half season of stats.

And why am I including all this discussion? Because deciding a field of participants is a important (it's a big televised event and NBA players have to be scheduled and showcased), and it's unwise and apparent that only looking at a half season does not lead to the best set of selections.

Another important observation is the inherent noisiness of the results. The "pseudo" R-squared from the model was 0.1163, which equates to saying only 11.6% of the variable is explained by the variables. For those unfamiliar with regression, that's a low number. Basically, determining the winner of a single round is close to a roll of the die. It's important when watching the contest or looking at past results to realize it's not telling you who the best shooter is, despite the advertisement. Luck and chance are huge factors.

If you're looking for other variables I missed that would explain the results, I'm listening but I considered many. Shooting style is one I've spent time on, but there doesn't appear to be a pattern either. Set shooters win? Beal nearly won and he jumps higher than most do in the contest, and Ray Allen won in 2001. And players with lower verticals like Peja, Pierce, and Bird have excelled as well.

Simulation model

With a formula in hand, the next step is simulating how rounds are won. There is no directly solvable way to estimate a player's chance of winning because there's a great deal of variation in what every player will shoot, you need to simulate the finals based on how well they do the round before, tiebreaker rounds are possible, and which players are in your bracket affect your odds.

Considering those facts, the model was built by varying the coefficient for shooting percentage based on the standard deviation from the regression results. Essentially, this gives a "real world" set of varying results where Kevin Love can shoot 20 one round and 12 the next. With the link logit (beta) form, there's also realistic limits where a score of 25 or 26 is rare, and so are scores of 4 and 5. Out of the 90 first round scores from 2000 to 2013, there was only one score of 23 or higher (Arenas) or 1.1%. Running the simulation with 32,000 games played (4000 simulation seeds with 8 players), there were only 326 such games, which translates to 1.0%. Only one case doesn't prove the model is reflecting real world conditions, so I plotted a histogram below showing how the first round in the real world and virtual compare.

Since the simulation used only the 2014 participants for their shooting percentages, it's not a perfect representation, but it's showing a reasonable spread of results. There are only 90 results in the "real world" first round, but the shape is starting to form a normal curve and one can see that rare events in the simulation are indeed rare in reality.

Based on the simulation results with 4000 random seeds for each of the first rounds and 32,000 for the finals (4000 separate simulations for each possibility of the 4000 first round seeds), the most likely winner was Stephen Curry at 24.6%, followed by Beal who was closely followed by Lillard and Belinelli. Love's odds are at 5 percent, but that's largely due to a terrible 2013. On the other hand, the metric is weighted by attempts, so an injury-plagued season is not as destructive. Afflalo also has low odds due to a low percentage the previous season. Beal's advantage is in an easier field; he's not necessarily better than Belinelli. While Curry disappointed again, his odds were not near 100%, of course, and the finals had two guys in the top four by this simulation.

Simulation odds for winning the 2014 three-point contest
Damian Lillard
Marco Belinelli
Kevin Love
Stephen Curry
Kyrie Irving
Joe Johnson
Bradley Beal
Arron Afflalo

Bovada (Vegas), by the way, provided an interesting set of odds. Curry was given 2 to 1 odds, which is significantly more than I estimated and there's a huge gap between him and the next closest competitor. I'm disappointed I didn't try this method sooner because Bovada listed Beal with the second worst odds of winning even though he's shooting 43% right now and shot 39% last season (and he's in an easier bracket.) I would have labeled this the best value along with Belinelli, and since Belinelli ended up winning with Beal second you could have put down 20 dollars on him and won 120.

Alas, the one contest that didn't need an overhaul was changed, and the new maximum score is 34 because there is a moneyball-only rack you can place at your choosing. Since this increases the variability of the score, the odds should be closer together. I'll have to think of a way to emulate this behavior before the next contest.

Under- and over-performing shooters

An regression output that's usually ignored is the list of residuals. This is basically every single observation in the data (every single player's round) and it's usually squared or standardized. It's a way to spot outliers and trends. Though in this context, we can see which players are consistently doing better or worse based on their shooting percentages and what round it is.

The table below includes every player with at least four rounds from 2000 to 2013 and their average model error. In this case, a positive error is good; they're over-performing in the contest based on their percentages. A negative error means they're shooting worse than you'd expect. Based a robust sample of 11 rounds, two contests won, two others coming in second place, and outperforming the model by an average of 3.6 points, Peja may possess a little extra magic we can't quite capture by stats. Arenas is entirely buoyed by scoring a 23 in the first round and never repeating a similar feat, but it was only 4 rounds. Billups and Nash, unfortunately, never lived up to their reputations. Nash is currently 9th all-time in three-point percentage, but his average score was 14.

Residual errors (not squared) for players with at least 4 rounds, 2000 to 2013
Total rounds
Average error
Peja Stojakovic
Gilbert Arenas
Voshon Lenard
Daequan Cook
Jason Kapono
James Jones
Kevin Love
Quentin Richardson
Ray Allen
Kyle Korver
Wesley Person
Dirk Nowitzki
Kevin Durant
Paul Pierce
Rashard Lewis
Chauncey Billups
Steve Nash

Speaking of all-time great shooters who have not done well, I expect many people will bring up Curry, but he hasn't strictly been disappointing. Before the 2014 contest, his average score was 17.3 with an average error of 0.8; he actually did a little better than expected. However, he was very consistent so he's never had a huge score, but on average he does quite well. Translating his 16 point total from 2014 to the old rules with less moneyballs, that roughly equates to 14 points, which isn't a disaster. I'd say he hasn't underperformed based on his shooting skill. The problem, rather, is that people view number one ranked players and teams way too high compared to the field in most situations, especially in this contest where the results are noisy. 

If the league wants the best shooters possible, they can't ignore previous seasons of data about three-point accuracy. And I'd suggest letting three players play in the finals again because luck is too much of a factor already.

As a final note, I want to comment on the strategy of where to place the moneyball rack. Players are afraid of using it in the last corner because they fear they won't be able to finish the rack and could waste the extra points. This is not an ideal strategy for a few reasons. Besides how much closer the line is and how most players shooter better from that distance, players either finish rounds or have the clock expire as they reach and try to shoot the last ball. It's rare that a player leaves two or more balls unused, Joe Johnson notwithstanding. But no matter where you place the rack, the last ball will always be a moneyball, and if you're afraid of time expiring with two or more balls left you won't have a good chance at advancing anyway. (Although I'd prefer the old rules be reinstated because we have so few era-neutral basketball aspects to judge players. Adding moneyballs only increases luck and variability, and this contest already has that in spades.)

Monday, February 10, 2014

Kevin, It's Not Your Fault

History has given us important lessons that we soon forget. With Minnesota in danger of missing the playoffs again while Kevin Love delivers one of the most impressive seasons in recent years by most metrics, we're going to be treated to countless arguments about how he doesn't deserve to be on an all-NBA team or an MVP ballot because "he" failed to lead his team to the promised land. But before we use some archaic, color-by-numbers approach to evaluate a player, we should use some perspective and context -- this is a time when an ocean of statistics are available and every play is available for viewing online. We can do better than "his team didn't make the playoffs."

Looking to history for similar situations

Years ago, one of the most intimidating, giant, and highly skilled players, an MVP and statistical monster, missed the playoffs in two straight seasons during his prime, even while playing heavy minutes. The conventional wisdom has always been that you cannot be a superstar if you fail to lead your team to the extra-season, but he broke those rules and caused people to question either how good he was or how we perceived team success. It was difficult to argue what more he could have done because he filled up the stat-sheet in numerous categories with gargantuan numbers. Was he not a leader? Was he only playing for his own stats? Later, when paired with better players, he coincidentally won a championship and the critiques faded away; he was now a "winner."

Today, that player, Kareem Abdul-Jabbar, is regarded as one of the best players of all-time. The consensus is that he's at least the third best player ever and maybe even higher. Media figures like to use the line "you can't be a star if you can't lead your team to the playoffs," including Bill Simmons, who said back in 2006 said, "Can you name another alleged "superstar in his prime" who missed the playoffs for two straight seasons?" Somehow he missed Kareem, and this is a guy who watched Kareem in person back in the 70's and wrote a book on the history of the league.

And it's not just Kareem. Barkley missed the playoffs in 1988 and won only 36 games, while repeating the feat in 1992 with 35 wins before being traded to a better team the next season, where he immediately went to the finals (that should sound familiar.) Moses Malone, loved by the old-guard and the mainstream media, missed the playoffs in 1978 and won 41 and 40 games, respectively, in 1980 and 1981, though he still managed to pick up three MVPs between 1979 and 1983. He also joined a talented 76ers team with a superstar in Dr. J along with a great supporting cast in Bobby Jones and Mo Cheeks, among others, and they had been going deep in the playoffs every season, including three finals appearances in the six seasons before Moses.

Kobe missed the playoffs in 2005 after Shaq left and nearly missed again in 2006; the 2014 Wolves may actually match their win total but could miss out due to the abnormally strong western conference. Olajuwon had four straight seasons of mediocre 40 to 46 win teams while he was in his 20's, but always gained entry to the playoffs due to the weak conference at the time, except later in 1992 when, in the prime of his career at age 29, they managed a meek 42 wins.

Some of the greatest players of all-time have either missed the playoffs in their prime or failed to clear the 0.500 Mendoza line. If even the best players ever near the top of their games can't push every team they have past 45 wins or worse, then why can't Kevin Love be an elite player when they're on pace for 40 to 42 wins with a point differential that suggests an even stronger team? People will cite how he's never been to the playoffs and will even include his 2013 injury-plagued season, but they fail to study the context, which is especially relevant for one of the most incompetent franchises in the modern era.

But the most similar case, arguably, happened with the same franchise less then ten years ago and they, oddly enough, share the same first name. Kevin Garnett missed three straight playoffs while averaging 22 points, 13 rebounds, 5 assists, 1.5 steals, and 1.4 blocks a game. In fact, Garnett's only one of five players in NBA/ABA history who have led their teams in total points/rebounds/assists/blocks/steals, and he narrowly missed the feat (by only six blocks) in 2005. He also missed the feat in 2006 by a measly four steals. He missed the playoffs, but what more could he have done as the leading scorer, passer, rebounder, and defender?

If you thought Garnett was just a "good stats on a bad team" player, then the ultimate test would be to put him on a team with good players and a good coach, and, obviously, that's exactly what happened. The result, even though Garnett was a little past his prime? A dominating season with one of the best modern defenses of all-time, and a championship that included several lopsided wins. (People cite their losses, but overall a point differential of 5.2 is very strong even for a title team. Hollinger rated it as the 10th best finals team in league history in 2011.)

We've had a prime example of a power forward named Kevin putting up monster numbers for a non-playoff team where people feared he really wasn't a winner, and it was thoroughly refuted, proving that a more comprehensive and thoughtful analysis is needed than "his team didn't go to the playoffs."

Kevin Love's incredible season

People generally rate players based on their basic box score averages, however flawed that is. He excels here since he does pretty much everything on offense, including things not captured by basic stats, and he remains one of the best rebounders in the league.

25.6 points, 13.3 rebounds, 4.0 assists.

The list of people who have matched those averages is extremely short. Only Wilt Chamberlain, Elgin Baylor, Billy Cunningham, and Kareem Abdul-Jabbar have averaged at least 25 points, 13 rebounds, and 4 assists a game. And those seasons were aided by the high pace of the early NBA, inflating rebound totals and other stats. In fact, looking at per possession stats, no one else matches Love's combination of high volume, high efficiency scoring, elite rebounding, and great passing: a usage of at least 28, a TS% above 58, a rebound rate above 19, and an assist rate above 19 (via basketball-reference.) Relaxing the thresholds to a usage of at least 27 (remember league average is 20, and the league leader is typically a little over 30), a TS% of 56 (above league average comfortably), a rebound rate of 18 (still elite, especially for a high scorer), and an assist rate above 17 (assisting on 17% of his teammate's field goals), there are a handful of seasons that qualify since turnovers were first tracked: Kareem in '78, Barkley in '95 and '96, Shaq in '00 and '01, Duncan in '02, '03 and '07, Garnett in '04, and now Kevin Love in '14. That's a list of some of the greatest, well-rounded big men in league history, and Kevin Love is among them. These "one of X players to average this" lists can be arbitrary, but those are all important stats and the standards were relaxed significantly under what Love is doing now.

People counter that Love's points come from "garbage" plays and he only scores in situations like an offensive rebound put-back. It's frustrating to hear that criticism because this is an era where we have an dizzying array of stats and video available. Then there's something basic like his offensive rebounding numbers this year have been low, under 10%, partly due to all his outside shots. Synergy, for example, tracks play types, and you can quickly check how often he scores off cuts, offensive rebounds, post-up plays, etc.

Love favorably compares to other noted interior scorers. Although Kevin's post-up plays are fairly low relative to some of the other scorers, he's in the range of Blake Griffin and Duncan, and he manufactures more of his points from spotting up. If you're going to argue that this is somehow less valuable than posting-up, ignoring how often Love does produce in those plays, then I'll point to the success of many, many modern teams who don't use their big men in this fashion and take more three-pointers than average. Also, as Zach Lowe has pointed out here (near the bottom at number 8 in the list), his accurate outside shooting perplexes and stresses defenses, making it easier for his teammates to score. And by trading in a few post-up plays for spotting up behind the line, he's also more efficient than otherwise and draws out big men from the paint. As the final nail in the coffin that Kevin Love isn't a "real" scorer and lives off garbage shots, he generates 8.3 free throws per 36 minutes, which has only by done by only five other players this season: Durant, Griffin, Harden, Cousins, and Howard.

Offensive rebounds
Kevin Love
Per game
Points per play
Blake Griffin
Per game
Points per play
DeMarcus Cousins
Per game
Points per play
Tim Duncan
Per game
Points per play
LaMarcus Aldridge
Per game
Points per play
Zach Randolph
Per game
Points per play

If you believe Love's numbers are somehow empty, then we're talking about "impact" and there are a new breed of statistics for this -- plus/minus. If his numbers don't actually help his team win, then we shouldn't see an appreciable difference for when he's out of the game; but this isn't true. When he's on the court, Minnesota scores 111.6 points per 100 possessions, which would equate to one of the best offenses in the league, while off the court the team falls to 98.0 points per 100 possessions, which is worse than any other team in the league. They're also a little better defensively when he's on the court, translating to a 16.6 difference when he's on the court compared to off.

If you think that's because of a bad bench and Love sharing minutes with Pekovic and Rubio, one website, talkingpractice, has three different versions of adjusted plus/minus showing that he's indeed a very valuable player. One method calculates a prior rating from a machine learning statistical plus minus to use in an RAPM model (meaning, it's a mix of traditional stats and plus/minus), and Love is ranked third in the league just behind Durant and Curry. But if you want a "pure" plus/minus that knows nothing about a player's stats and only how his team performs when he's on the court compared to off, his non-prior informed RAPM is very strong at +3.6, while another method that uses only a previous season's RAPM as a prior has him at +3.9, which is 12th in the league. Simply put, there is no evidence to suggest that Love's stats are empty and that they don't translate to the team level because, even with advanced models using different methods, his team is much better when he's on the court.

Video evidence

With the availability of video of every play this season, it's lazy to call Love a garbage scorer without first checking the evidence. To remove any doubt he does have a post-game, if the stats weren't enough by showing his post-up frequency, the fifth video in this playlist shows Love going up against the best defense in the league and a good defender in David West. He gets the ball from Rubio, backs his way closer to the basket, and makes a hook shot over West in the paint. 

So yes, Love can score in the post even over a tough defense. Going through the video, he uses the hook shot a lot, including a running hook across the paint. Another common weapon is a turnaround jumper that utilizes his nice touch. He can also seal a deep post position, and can either score easily over his shoulder or use pump fake and get to the line. But his real value is the diversity and range of his skillset. Look at this playlist of shots versus the the top five Warriors defense. There's a hook shot from far away. Then he drives hard to the basket finishing over Bogut. The third video is a contested jumper in David Lee's face, and the fourth a turnaround jumper similar to Aldridge's prime move. Then in the fifth video he shows off his running hook across the lane More of his repertoire is shown below in a game versus the Thunder (a top five defense with good big men defenders.)

Faces up against Ibaka, drives to the rim, pump fakes, gets fouled and makes the shot:

Faces up against Ibaka again, one min. to go and down by two, drives, spins and hits hook shot:

Fights for position against the much bigger Steven Adams, ends up one foot from the basket, easy lay-in:

Spin move against Ibaka while down two points with 27 seconds to go:
If you're not convinced, you can watch him torch one of the other best defenses in the league (the Spurs) for 42 points, and yes, Duncan was playing (though Splitter was out.) He's a devastating offensive player because of his wide variety of moves, use of space, awareness of where to be, and his range. He's great at stepping back quickly behind the line and draining a shot, putting undue pressure on the defense because they're not used to a power forward who can hit those shots -- especially not one who can also handle the ball, drive, or pass. He's the modern 25/13 big man everyone has been waiting for, posting up, crashing the boards, or draining shots from 24 feet. To add insult to injury for any defense planning for him, he's one of the best passers in the frontcourt, and one of the best outlet passers since Wes Unseld.

(Sources: here and here.)

There is some merit to the criticisms about his defense, but they're usually overblown. Players lacking athleticism and size face an unfair amount of negative attention for defense, but if they compete and stay with their team's principles they're usually not liabilities unless they're very old. The best example here is Steve Nash, as most people didn't realize Phoenix's real problem on defense was Amare Stoudemire inside. The most reliable defensive stats we have now come from plus/minus data, and referring to the Talkingpractice blog Kevin Love is slightly above a net zero (i.e. league average.) His Synergy stats are nothing troubling either: 0.88 points per play allowed, with 0.74 on isolation plays (ranked 73rd overall) and 0.73 in the post (ranked 42nd.) For reference, league average offenses like the Knicks score 0.93 points per play overall, and the numbers of iso and post players are generally above 0.8. He's also taken 11 charges this season, tied for sixth in the league. Evidence of Love being enough of a liability to wash out his impact on offense is lacking, unless people (unfairly) rely on things like athleticism and skin color.

Kevin Love is not their problem in the clutch

While people criticize Love for not being able to lead a team to the playoffs, it would surprise most that by some basic measures of team strength Minnesota is actually one of the strongest teams in the league, with a better adjusted point differential than teams like the Miami Heat. Currently, the Wolves are eighth in the league in SRS, basketball-reference's simple rating system, which should translate to a 32-19 record and a 6th weed. Unfortunately, they're 24-27 in reality, ranked 11th in the west. How does a team's point differential not match with their win-loss record? Typically, it's either from a strange number of one-sided blowouts or a team losing/winning most of its close games. Minnesota is 1-12 in games decided by four points or less, while they've blown teams out by 15 or more points twelve times but only lost by 15 or more two times. They've lost so many close games it's unlikely to stem from "luck." Blowouts, however, are still important to judge the strength of a team going forward, including how they do in the playoffs, but their collapses in close games are troubling.

(Note: people will usually cite the 4 points or less statistic because the Wolves are 1-1 in games decided by five points, and 1-12 looks worse.)

Kevin Love's TS% in clutch situations, where there are five minutes left in the game and the score is within five points, is only 47.9, but shooting percentages dive in these situations league-wide. It's a few spots below the league average, but for comparison Aldridge shoots 47.6%, Dragic 47.4%, and Joe Johnson, whose all-star berth was buoyed by recent game-saving plays, also shoots 47.4%. So to say that Minnesota is one of the most abnormally bad teams in the clutch because of Kevin Love is misguided. He increases his usage rate from 27 to 31 (he's not shying away from the spotlight) and his shooting percentages are not far from the league average in close games and from other star players who are known for heroics.

The culprit here is Rubio. He has been an outright disaster. Using stats.NBA.com's tool and only for players with at least 10 of these games with three minutes per game, Rubio is ranked 151st in the league out of 155 by the NBA's all-in-one "PIE" metric. (Their equivalent of PER.) He has an effective field-goal percentage of 16.7 percent in these situations and a 31.9 TS% (the TS% is boosted by Rubio going to the line for intentional fouls.) Needless to say, 16.7 percent is awful. And their backup point guard, Barea, has been even worse with a 14.3 TS% and fewer assists than Rubio. Kevin Martin and Brewer shoot well in those situations though, but they're feeding off either Rubio or Love. Having a point guard who's a historically bad shooter is enough of a burden for an offense, as defenses can key in on the other players on the court and sag off Rubio, but one who's even worse when the game is close can stall an offense. And their fast break attacks are less common when the game slows down due to all the timeouts. Their offense is a little less effective in the half-court.

The offense, however, with Love shouldering a large burden with the wings Martin and Brewer shooting well, isn't the worst part. Their offensive rating with five minutes to go and within five points is only 99.1, according to stats.NBA.com, compared to their overall average of 104.7, but their defensive rating is 134.9, a ridiculous number, to put it simply. Of course, people love to blame the star for a team's defense, and not anyone else, but there's more at work here. Opponents shoot 45% from behind the arc compared to the season average of 36% -- it's hard to say that's all Kevin's fault here. If you want to blame Kevin's lack of rim protection, opponents are scoring 32 points in the paint per 48 minutes in the clutch; the season average for Minnesota opponents is 45. Ergo, the defense doesn't collapse from his inability to stop players inside. Another factor is that the opposing team is pulling down an obscenely high rate of offensive rebounds: 37.5% of all available boards. But, again, it's not Kevin at fault here: he pulls in an elite 26.6% of defensive rebounds. Pekovic (and Rubio) are the ones whose defensive rebound rates fall by almost half, and Dante Cunningham, who replaces Love when he's out sometimes, rebounds like a point guard when the game is close.

Historically bad shooting from their point guards and bad luck have given Minnesota a terrible record in close games. The prime example was a game against the Mavericks they lost by two points where the last play of the game was a Marion "block" on a Kevin Love jump shot. In fact, the NBA admitted they made a mistake in not calling a foul, which would have sent Love to the line for two free throws to tie the game. Also, many of these close losses come from games where the Wolves have a sizable deficit and claw their way within striking distance but come up short when time expires; it's not that they're choking away games they already game. For example, with three minutes to go against the Kings, they rally to come within just one point, thanks to plays like Love's back-to-back three's. (This partially explains the horrible defensive rating because they have to intentionally foul.)

Minnesota has lost an unusually large amount of close games, but the cause does not appear to be Kevin Love. Opposing teams are hitting their three's and crashing the offensive boards, but Love's the only one rebounding well. We can't expect the Wolves to keep losing all their close games at such an absurd rate; the magic of regression to the mean will lift their record. Even Rubio can't keep shooting that poorly.


If Kareem, undoubtedly a top three player of all-time, can miss the playoffs two seasons in a row in his mid-20's during a time when many of the best players were in another league, and still not lose his spot in the Pantheon, then why can't Kevin Love be a top four player during a single season? Kevin Garnett matched the same feat three straight seasons with the same Minnesota franchise. To simply throw away Love's candidacy for all-NBA for top five MVP consideration because his team missed the playoffs in the toughest western conference ever, arguably, is lunacy. If you think Love can't lift a team with a few decent players, then you're ignoring how Minnesota is 8th in the league in adjusted plus/minus -- he's doing a better job, using one basic but effective team metric, than LeBron James! When Kevin's on the court, his team's offense is devastating. To ask Kevin to do more, that it's his fault, is bewildering because of how much he's already doing. It's not his fault Rubio is a terrible shooter and is even worse when the game is on the line.

We haven't learned from Kevin Garnett's case, which was a fairly recent occurrence. I do not have confidence the media and fans will treat Love appropriately. We place too much credit on the star players for team records, especially in close games. To say that Love is choking is odd because his shooting percentages mirror that of Aldridge's and Joe Johnson's. Maybe one day we'll learn. But with Minnesota's playoff chances dwindling every game, we're going to see more press about how Love isn't a "winner." And it's not fair.

Kevin, it's not your fault.