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Archive for the ‘Advanced Stats’ Category

Things didn’t turn out as expected in Milwaukee. It’s difficult to predict a team losing 267 player-games to injury. It’s crazy to expect the shooting percentages of an entire team to crash down to near career-low levels. But the Bucks found themselves facing down both disasters this season, and the results were often ugly.

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Of course, you don’t encounter those problems without a bit of bad luck. Or a lot of bad luck. In this case, that luck is theoretically illustrated by the gap between the two lines. That upper red line follows Milwaukee’s Pythagorean Expected Win Percentage, which remember is based on point differential. As is turns out, Milwaukee’s final point differential was typical of a thirty-eight win team. This season, 38 wins earned you the 8-seed in the Eastern Conference Playoffs.

This bad luck often gets explained by a team’s record in close games. The Bucks were 9-10 in games decided by five points or less. Of course, final margin might not be the best way to consider a team’s success in “close games,” so what if we consider Tom Haberstroh’s modification? Expanding the “close game” moniker to all games that are within five points anytime during the last five minutes, the Bucks’ record becomes 22-25.

Both percentages are close enough to .500 that it’s not totally unreasonable to blame bad bounces for dragging down Milwaukee’s record. After all, we’re talking about games decided by a bucket or two one way or another. A few more misses by Milwaukee’s opponents or a few more makes from the Bucks and we could be talking about how much they outperformed expectations.

So the Bucks were apparently a little unlucky, but the graph shows another interesting trend. Namely, Milwaukee actually had a winning record after the All-Star break. In fact, Milwaukee’s 14-13 record gives them a .519 winning percentage after the break, which outperforms their Pythagorean expectation over that same stretch by about 0.6% (Milwaukee outscored it’s opponents 2510-2501 in total post-break). If we say a few magic words, toss some glitter in the air, and extrapolate that sample out to a full season, the Bucks grade out as a 45 win team.

FORTY-FIVE WINS!

I can’t help but be a little excited by that number (which is a bit sad in itself). Obviously it’s treacherous to trust small samples, but there are reasons to believe 45 wins is a better measure of this team than 35. For starters, they finally started to get kinda-sorta-healthy after the break. They shot a little better while their opponents shot a little worse. They fouled less and forced more turnovers. In general, they looked much more like the team that won 46 a year ago.

Unfortunately, the only thing this winning stretch accomplished was worsening Milwaukee’s draft position. Pre-break, the Bucks were on pace to win only 31 games, which sounds awful, but would have bumped them up two spots in the draft. It’s always something, isn’t it? Still, these numbers suggest that–with a little luck–Milwaukee may indeed wake from this nightmarish season next year.

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Value seems like the buzzword of the hour. The focus on ascribing some definitive number to every part of a team’s construction and performance has become measurable, visible in the databases filled with box scores and batted-ball spray charts. Suddenly even casual sports fans have trouble navigating the culture of their favorite teams without encountering some formulation of acronyms stuck to the front of a “-metric” suffix. Some (though perhaps fewer and fewer) lament the passing of days where a complete understanding of sports required no knowledge of regression analysis. While public opinion might not always follow along with willful enthusiasm, remember that the original motivation behind “advanced metrics” was to achieve a deeper understanding of what constituted success. Doing so proved an exceptional method of winning, a common goal for both the front-office brains and the fans who watched unorthodox methodology deliver the same result they always hoped for. Winning, after all, was the ultimate goal for everyone involved, and this value-based system was simply the latest tool.

The numbers sum up everything. They don’t value rough-and-tumble defensive stoppers, they value low defensive ratings. They don’t value 30-point scorers (er, kinda), they value 16 points on 10 shots. Sure, that’s overstating and oversimplifying things too much, but there’s a reason efficiency gets all the face-time these days. Efficiency gets results on the cheap. Efficiency doesn’t blow leads or hog the ball. It’s not sexy, but it gets the job done. Efficiency gives owners what they want: value.

One could say, then, that value is just little bits of winning. We grant that a player is valuable because the things he does help his team by a (reasonably) determinable amount. The only reason the numbers want Kevin Love to grab a rebound is because it has some specific value which, accumulated in high enough numbers, will help earn his team a win. It’s a mildly harsh reality, reducing the actions we see to parts of a sum, but it’s one that more and more people are warming to, myself included. I’m happy to grant that per-possession statistics are far more valuable than their per-game counterparts, or that protecting scoring opportunities is exceedingly important in winning basketball games. I’ll happily agree with anyone who says that maximizing the value of those shot attempts is an important factor in winning a basketball game, and that high-volume shooters might actually be deviously undermining their team’s success.

Given all that, it would appear I have managed to convince myself that I am kidding myself when it comes to Brandon Jennings. When Jennings fell just shy of a triple-double in his first career game, it jolted me out of my chair. When he dropped 55 points on the Warriors two weeks later, it sold me. It sold me so well that even as Jennings fell back to Earth over the next few months, I remained stoutly convinced that he was the future of professional basketball where I was concerned. Following that season, I started writing this site under a name inspired by his performance, even as doubts over whether it was all a cruel joke grew in my mind.

If you’re looking for an understatement, let me say that Brandon Jennings has experienced a drop-off since those torrid first weeks of his rookie season, to the point where there are times when the Milwaukee Bucks win in spite of him, rather than thanks to him. As that first season rolled along, it pained me to see criticisms of the team, identifying the frequent nights when Jennings would “shoot Milwaukee out of the game.” Why was I so affected by such scorn? After all, the number-disciple in me sided with the critics. I had no vested interest in Jennings outside his role as the starting point guard for my favorite team. I had no affiliation with the team beyond  that of a particularly interested fan, but I hated that every shot taken by Jennings would invariably lead to some shot taken at him. Yet through it all, my enthusiasm for his play never waned. It was cognitive dissonance wearing a #3 jersey. Screw value, I thought. Efficiency be damned, this kid is fun.

Is that irresponsible? Probably. Professional basketball is a business, where personal attachments only count for as long as they’re convenient. If Tim Duncan wasn’t the greatest power forward who ever lived, he probably wouldn’t have stayed in San Antonio his entire career. Draft picks staying with the same team for a full career isn’t exactly the norm. Is Jennings good enough to warrant the title of “Franchise Point Guard” in Milwaukee? That’s not a decision to be left up to me. But I can attest that Bucks basketball hasn’t been the same since Brandon Jennings joined the squad. He brought with him the most exciting performance and season in years. He has a dramatic flair and unquenchable attitude. Despite his undeniable struggles, when he has the ball in his hands, I always feel like something really, really cool could happen. Don’t tell me there isn’t value in that.

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I was reading the latest article up on Hardwood Paroxysm, regarding the infamous “long 2” and it’s role in offensive and defensive efficiency, and was intrigued by the apparent disconnect between above- and below-average defensive teams and the percentage of long 2s they force opponents to take. As described in the article, the correlation between forcing long 2s and better defensive efficiency seems much stronger in below-average defensive teams than the elite defensive teams. Why is that? Why does forcing long 2s suddenly become less important when you start talking about the best defenses in the NBA?

I’d warrant that the key difference is this: the best defensive teams in the NBA don’t always need to force long 2s. The conventional wisdom (and, by extension, the statistical framework) doesn’t apply equally to good defensive teams. That sounds illogical, though. If long 2s are indeed the least valuable shots in the NBA, why would any team not pursue the advantage of forcing opponents into those shots?

Let’s not blind ourselves by looking exclusively at numbers. Consider those teams in the “elite” defensive efficiency range. Each has the benefit of a standout defensive player or players, an exceptional defensive system, or most likely a combination of both. San Antonio has Tim Duncan, Orlando has Dwight Howard, Boston has Garnett/Rondo, Cleveland had LeBron. Milwaukee has a great system plus perennial DPOY-snub Andrew Bogut. The Lakers have a great coach and a bunch of good defenders. Charlotte, Oklahoma City, and Miami all had solid defensive schemes and players in place.

The point is – and I don’t think it’s too much of a stretch – those teams all have factors contributing to their defense that are far more important than opponent shot distributions. In improperly-applied mathematical terms, the defensive efficiency of above-average teams is more a function of their own ability than their opponent’s shot selection. Meanwhile, those teams who can’t (or don’t) play high-caliber defense are far more subject to the performance of their opponents. Conceivably, the efficiency rankings for those below-average teams are a result of which teams randomly gave up more high- vs. low-percentage shots. Obviously this isn’t the exclusive factor – we can all agree that basketball is more complex than just shot selection, and we would expect the graph to be perfectly correlated in such a one-variable system. But the correlation is strong enough to suggest that affecting shot selection is a useful defensive technique. It is, however, still subject to an occasional hot-shooting night from the opponent.

In Basketball on Paper, Dean Oliver explains how inconsistency tends to drag teams toward a .500 record. The discrepancy in HP’s graph suggests that the best defensive teams avoid such inconsistency by excelling at factors that are easier to control. Maybe they have a good shot-blocker, or they defend pick-and-rolls well. If you imagine an idealized team that blocks a very high percentage of shots at the rim, it might even be advantageous to funnel opponents inside, rather than forcing long 2s. The point is, good defenses force opponents to conform to a game-plan that favors the defender. Teams who can’t do this tend to perform better when opponents shoot bad shots. Thus, the best teams can play effective defense independent of opponent shot selections.

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Happy Holidays everybody!

Milwaukee took two of three on their West Coast Tour, and not the two you might think. That leaves the Bucks at 6-5 in the month of December – 6-5 in a month that I earlier speculated might not yield 4 wins. It sucks knowing that we might not see Brandon Jennings back on the court until February, but this team has shown, once again, that tenacity and raw effort can go a long way. It’s been a frustrating season, for sure, and while beating the defending champs in a shockingly-sound manner makes Milwaukee fans pull their heads out of the snow, there remains work to be done. Milwaukee is still four games under .500 and now faces a Hawks team looking for revenge before kicking off what is likely the most difficult five-game stretch of the season.

Milwaukee is now closer than they’ve been all season to outperforming their Pythagorean Win Percentage. Unfortunately, those numbers still only peg the Bucks for 37 wins. Personally, that seems low to me, especially considering how easy Milwaukee’s schedule gets, but Pythagoras cares little for such hopeful wishing. We can hardly call the Bucks contenders, and the preseason expectations seem a far cry from where they currently stand, but this is a team that can absolutely win any game they play. They might not win the NBA Title, but you can bet there’s gonna be a few more times this squad will embarrass some elite team, prompting many declarations of “Fear the Deer” and drawing minor attention to Andrew Bogut’s status as a top-3 defensive player in the NBA.

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A couple of numbers: some surprising, some not; some arbitrary, some revealing; some reassuring, some hopelessly, hopelessly depressing.

  • Larry Sanders has the best Defensive Rating on the Milwaukee Bucks at 96. Andrew Bogut is second at 97. Newcomer Drew Gooden clocks in third at 101.
  • Brian Skinner is the only player currently on the roster with a DRtg above 105. Yes, that includes Corey Maggette, who is tied at 105 with Ron Artest.
  • Since the NBA/ABA merger, only 4 teams have ever shot worse over the course of a season than the Milwaukee Bucks are currently shooting (.410 FG%). The ’01 Warriors (.409), and the ’99 Hawks (.409), Nets (.406), and Bulls (.401). The ’99 NBA season was shortened to only 50 games, and the Hawks somehow made it to the Eastern Conference Semifinals.
  • Jon Brockman is leading the Bucks in Win Shares per 48 minutes with 0.174. Chris Douglas-Roberts is second with 0.169. Keyon Dooling is last at .026.
  • John Salmons is third on the Bucks in Defensive Win Shares with 1.1. He has produced -0.3 Offensive Win Shares.
  • If John Salmons makes his next 35 shots without missing, he will be shooting at his career percentage of 44.3%.
  • The Bucks are on pace to shoot 2364 free throws this year. That would be tied for 9th most in franchise history. Of the nine Milwaukee teams to shoot at least 2364 FTs, two won more than 60 games (any guesses as to which years?) and eight won more than half of their games.
  • By using their Pythagorean Record Expectation, the Bucks are predicted to win 37 games this year.
  • If the Bucks shot league-average from every spot on the court, they would be the 12th-best shooting team in the league. Miami would be the worst.

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Excuse me for a second.

<Tuuuuuurrrrkeeeeeeeey….graaaaaavvvyyyyyyy…..uugggggghhhhhhhhh……>

Good. Now that I’ve got that out of the way, here’s an update on the Milwaukee Bucks’ Pythagorean Stats:

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See how good things were looking around Game 9? Yeah, that’s where we were last time. At the time, the theme was “How quickly things change.” The new theme is, “Good God, if Salmons kills another possession by picking up his dribble at the free-throw line I’m gonna scream.” While Milwaukee’s defense has stood fast as one of the best in the league (top ranked in ORtg, top-5 or top-10 in a host of other metrics), the offense has continued it’s hellish decent to the bottom of the NBA. The result? A somewhat counter-intuitive disparity between their true winning percentage and their expected winning percentage using the Pythagorean prediction, as you can see in the graph above.

While Milwaukee has lost four in a row coming into tonight’s game against fellow cellar-dweller Detroit, they’ve lost their last two by three points combined! Going through the whole season up to now, Milwaukee has an average point differential of +13 in their 5 victories and -7 in their 9 losses. In other words, when they win, they win big; when they lose, they lose not-so-big. Remember, the Pythagorean prediction is based on points scored and points against. While the Bucks’ true record has taken some hits, their 82-game projection still pegs them as an 42-win team. That’s a playoff team in the East (John Hollinger gives Milwaukee a 75% chance to make the playoffs), even if one game over .500 isn’t what we might have hoped for at season’s outset.

So while there’s reason for concern that Milwaukee is just never going to score points with ease, the self-assurances still have some value. The Bucks are not going to lose every one or two point game this year – the ball just doesn’t bounce that way. They’re not going to continue shooting 40% from the field. I repeat, they’re not going to continue shooting 40% from the field. It may be time to temper our expectations and realize that even this revamped team might not eclipse the feel-good story of last year’s squad, but it doesn’t mean the season is lost. I repeat (to myself), it doesn’t mean the season is lost.

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We’re more than 10 percent done with the NBA season already, which is a sad thought. But fear not, for I bring good tidings of estimated winning percentages, courtesy of our good friend Pythagoras (and also Bill James, Dean Oliver, and John Hollinger)!  As you can see below, before last Tuesday’s game against the Knicks, Milwaukee had an estimated winning percentage under 0.40. Just three days later, Milwaukee is expected to win almost 50 games! How quickly things can change when the ball starts finding the bottom of the net. This number will continue to change significantly for a while before starting to stabilize around mid-season.

The “True Winning Percentage” currently lags behind the expected percentage simply it is calculated from a smaller sample of games. Still, for now it’s interesting to see how winning games and point differential affect a team’s projected record. For those of you who are curious, I’m using an exponent of 14 in the Pythagorean expectation calculation. John Hollinger typically uses 16.5, but Dean Oliver explains in his book Basketball on Paper how 14 works well for slower-paced NBA teams. Frankly, you can’t spell slower-paced without Milwaukee.

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