Behind the Numbers: Who Is Killing Their Own Team?

Posted by KCarpenter on December 1st, 2011

A lot of the effort in basketball analytics goes towards the good things that players do that do not appear in the box score. This is the driving idea behind Michael Lewis’s seminal New York Times feature, “The No-Stats All-Star,” an early look at analytics in the NBA primarily focused on Darryl Morey, Shane Battier, the Houston Rockets, and adjusted plus/minus. This makes sense: finding hidden strengths is the coach’s angle while finding hidden value is the economist’s angle. As a result of the fine work of smart guys with formulas and others with a willingness to watch a lot of games closely, Charles Jenkins and Nate Wolters were household names last season. This, of course, assumes that your household is filled with basketball dorks, but you get the idea.

Faried Was An Underappreciated Star

Finding diamonds in the rough is a noble pursuit and talking up the greatness of underexposed and underrated players is a worthwhile task (Hey there, Kenneth Faried!). Sometimes, however, there is a joy in using analytics and “advanced” statistics to look for the guy who is hurting his team the most.  Let’s ignore the diamonds and go straight for the rough.

How does a player hurt his team? Well, when push comes to shove, there are basically only two ways: offensively and defensively. Sadly, however, contemporary box scores assign no grade for bad defense to the individual outside of counting how many fouls (which could very well be offensive) a player commits. Our primary understanding of player’s individual defense comes only in positive contributions like blocks, steals, and defensive rebounds while the effect on an opponents shooting percentage is measured at a team level. The noble effort of Luke Winn, David Hess, and others that has sought to enact Dean Oliver’s defensive charting schemes is a good start at really quantifying individual defense, but a very small percentage of Division I games have been looked at in this way making the approach of limited use to someone who wants to look at the whole of college basketball. So, acknowledging that analytic approaches to finding bad defensive players are limited, let’s at least take a quick look at fouls.

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Behind the Numbers: Our Robot Overlords Think We Give UNC Too Much Credit

Posted by KCarpenter on November 17th, 2011

Kellen Carpenter is an ACC microsite staffer and an RTC columnist. Behind the Numbers will publish weekly throughout the season.

Preseason rankings are a funny thing and once real basketball begins, these guesses about the season grow increasingly meaningless. People forget who was ranked where in the preseason by January, if they haven’t already forgotten by December. This, for what it’s worth, is probably a good thing.  Preseason polls can turn out to be pretty embarrassing, highlighting how little journalists and coaches actually know about how the basketball season is going to turn out. Remember Kansas State and Michigan State last season? They weren’t exactly huge factors in the postseason despite being almost unanimously ranked in the top five at the beginning.

Ron Morris Was Certainly On To Something

Gary Parrish’s “Poll Attacks” column is devoted to critiquing what he feels aren’t well-considered ballots. It’s an interesting idea and is usually very fascinating, but since Parrish can’t see the future, the columns have a tendency to not age all that well. What seems like understandable ridicule in November can seem less well-founded when the championship game is being played in April.  Here is a particularly infamous  passage from last year’s pre-season column:

Same dude voted Connecticut 18th.

This would’ve made sense three years ago, but it makes no sense now given that the Huskies have a coach who can’t seem to stay healthy, and a roster that looks nothing like your typical UConn roster. That’s why Big East coaches picked UConn 10th in the league, and why somebody needs to shoot me Ron Morris’ email address. With little effort, I can get him added to the official Big East email list, at which point he’ll start receiving announcements, and then this sort of stuff can probably be avoided. I don’t mean to be pushy.

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Behind the Numbers: Considering Point Guard “Purity”

Posted by KCarpenter on November 10th, 2011

Kellen Carpenter is an ACC microsite staffer and an RTC columnist. Each week, BTN will take an in-depth look at some interesting aspect of college basketball’s statistical arcana.

The phrase “pure point guard” is loaded. It implies that there is a Platonic notion of point guard which all mortal players can only aspire to. We are just fools in a cave looking at a shadow on the wall, but that is all we have when the purest conception of the point guard is beyond our field of vision. I can only assume that this unknowable figure looks something like Bob Cousy. It also implies that outside of “pure point” play, there exists a realm of impure play where the division of basketball labor isn’t as orthodox as it is inside Plato’s basketball cave.

This is What a Pure Point Looks Like

In a point guard, “purity” is code for being a pass-first lead guard. To the traditional school of thought, the roles on a basketball team are strictly regimented: The point guard passes, the shooting guard shoots, but not as much as either forward. The center, near-immobile but Mikan-like in his hunger for loose balls has a single task: rebound the basketball and get it to the point guard. Of course, this idea of the traditional division of labor in basketball hasn’t really held since the days of Mikan himself. Modern basketball, by which I mean basketball since the mid-sixties, has embraced the hybridization of positions. Basketball has for years acknowledged the idea that team roles are mutable and that positions are flexible.  While few have embraced the full-on positional revolution explicated by Bethlehem Shoals and the NBA-heads of the dearly-departed Free Darko, most of us have made peace with the idea that it’s okay for point guards to score occasionally. Kemba Walker and Jimmer Fredette were the break-out stars of the past college basketball season and both undoubtedly play point guard in a thoroughly impure way. If those guys aren’t pure then shouldn’t we all hope to be dirty?

In all seriousness, the concept of the purity of the lead guard is a silly concept to dwell on. Still, like all sports cliches, the idea persists because it’s a convenient way to sum up the play of pass-first point guards, who somehow pay homage to a golden era of basketball which is more than ancient history. Still the idea of the pass-first point guard is an intriguing one in this era of high-scoring combo guards. Like the crocodile, the pass-first guard is a relic of a by-gone epoch, a living fossil and a reminder of the dinosaurs who ruled the earth during that time. Is the crocodile a better predator than the tiger? This isn’t a debate that I’m interested in. The pass-first point guard, by mere value of their odd, antiquarian style is a unique species worth studying.

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Behind the Numbers: VCU & Butler Prove the Limits of Analytics

Posted by KCarpenter on April 7th, 2011

Kellen Carpenter is an RTC contributor. 
I should make my stipulations clear from the start: We should be talking Connecticut, but instead we will be talking about Butler. Before we talk about Butler, we will talk about Virginia Commonwealth University. The University of Kentucky, outside of this sentence, will not be discussed at all.

In a Shocker of Shakas, Smart Directed His Team to the F4

Continuing on: there is not a single analytic, logical or evidence-based approach that would have predicted VCU in the Final Four. Let’s be perfectly clear about this. In basketball analytics, most systems aim to predict likely future performance based on past performance and from that data calculate the most likely outcome. VCU in the Final Four was not a likely outcome by anyone’s reckoning. Sure, a few brackets had VCU in the Final Four, but that wasn’t because of rigorous analysis of match-ups or quantum wavelength formulas that are beyond us. Anyone who put VCU in the Final Four knew that it was an unlikely outcome. Maybe they put the Rams in because they were alums. Maybe their aunt lives in Richmond. Maybe they just think Shaka Smart is a handsome man (he is!). Maybe they picked the Rams because they knew few people would. All of these people who did actually pick VCU knew that it was a longshot as opposed to something that would probably happen.

This is smart. This is how you make brackets. Remember this. When there are thousands of different possible permutations, the most likely outcome is still pretty unlikely. An all-chalk bracket seems much more likely than any number of brackets in recent years, but it has still never happened. Hell, we’ve only had one year of all four number one seeds making the Final Four.  On a gut level do you feel that there is a significant difference between 1,000,000-to-1 odds and 1,500,000-1 odds? At the level of the infinitesimally unlikely, even big differences don’t seem to matter that much. I say this not as anti-mathematical nihilism, but to bring a sense of perspective to unlikely events. So here’s what I’m saying: when the most likely outcome is still incredibly unlikely to turn up, how surprising is it when something extremely unlikely happens? There is a real math answer if we gave these outcomes values, but the important answer, the one that we feel in our gut is that, no, it’s not really any more surprising than the other infinite variations of weirdness that the tournament spits at us every March.

Every bracket is a longshot prediction at a perfect bracket, which is such a rare and magnificent beast that not a single one was spotted this year (or any year, for that matter). In the ESPN bracket challenge, only two submissions out of 9.5 million even got the Final Four right. Long odds to get the bracket right, but of course, the odds that the teams themselves faced were not insignificant. Far smarter minds than mine have looked at the unlikeliness of the overall composition of this Final Four, the incredible journey of VCU to the Final Four, and the surprise of Butler in two back-to-back Championship Games; and while the supposed rarity and oddity of each of these accomplishments is interesting, it’s important not to lose sight of the big picture question: How did all these supposedly unlikely things happen and no one see any of them coming?

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Behind the Numbers: Structures and Strategies

Posted by KCarpenter on March 23rd, 2011

Kellen Carpenter is an RTC contributor.
Nate Silver isn’t always right, but I’m beginning to wonder why I would ever bet against him. Last week, Silver published a consideration of seeding where he argued that because of the structure of the bracket, the eighth and ninth seeds are at a considerable disadvantage compared to much lower-seeded teams. This makes intuitive sense because the way the bracket is constructed the eight and nine have to play a top-seeded team before everyone but the sixteen seed. Still, it sounds funny and it is odd that a twelve seed has a better statistical chance of making the Sweet Sixteen than any other seed between seven and sixteen. I was intellectually able to read and understand this logic, yet I ignored the fact that because of this quirk in seeding, George Mason was worse off in terms of having a shot at making the Sweet Sixteen than Virginia Commonwealth or Richmond

The Spiders Perhaps Weren't As Much of a Surprise After All

One eight seed made the Sweet Sixteen. The rest of the Sweet Sixteen party crashers? Two eleven seeds, a ten, and a twelve, including Virginia Commonwealth and Richmond. If you tally up the rest of the seeds, this looks pretty much like Silver’s predicted distribution. The structural inequalities of the bracket should have told us to expect more second round (excuse me, “third round” upsets) from the seeds in the 10-12 range. Of course, are these even really upsets? The Pittsburgh loss to Butler was a genuine shock but the rest of the “upsets” really seem to fall upon the coin flip in the flat part of the s-curve.  Silver notes that the composite computer “power ratings” show essentially the same difference between first and second seeds as between the fifth and thirteenth seeds. What this means is what we knew all along: the best teams are in a whole separate class from the bulk of the teams in the tournament, while the majority of teams are at close to the same level. This is a long way to get to this essential point: We shouldn’t be surprised to see VCU, Richmond, Marquette or Florida State in the Sweet Sixteen.

We also shouldn’t act like the bracket design is done affecting who makes the Final Four and who wins the championship.  Ken Pomeroy was quick to run the log5 probabilities of the remaining sixteen and had some interesting findings. While you would think that winning two games would have increased every team’s chances of winning it all, you’d only be mostly right. San Diego State and Kentucky actually saw their chances at a championship drop as the biggest obstacles in their path to the championship refused to be upset. Conversely, Kansas’s location in the decimated Southwest Region has made them a near-prohibitive favorite to make the Final Four. Likewise, Pittsburgh’s ignoble fall in an already weak Southeast Region has given the Wisconsin Badgers a real shot at a championship. The Badgers’ calculated chances of winning it all went from a mere 2.5% to 9.5%. Of course, technically, that’s a tiny increase in proportion to the change in VCU’s chances. The Rams went from having a 0.0005% chance at a championship to a 0.2% chance at winning the big one. While those are still long odds, their chances of winning increased 400-fold. So that’s worth something.

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Behind the Numbers: Against the Numbers

Posted by KCarpenter on March 15th, 2011

This is the one time of the year where people take an incredible interest in college basketball statistics. Folks who don’t know their Ken Pomeroy from their Jeff Sagarin rankings are suddenly asking how valuable a low turnover percentage is and if there is any evidence it correlates with tempo despite being allegedly tempo-free. Fortunately, there are lots of smart stat people who are willing to lend an analytic hand. If that’s what you are looking for, then let me point you in the right direction. 

Sullinger & His Buckeyes Perform Well in the Metrics

I obviously place a great deal of trust in respect in Ken Pomeroy’s statistical rankings that use Pythagorean expectation-based offensive and defensive efficiencies. Well, Ken has upped the ante by running a log5 analysis of the tournament field which breaks down the expectation of a given team to reach each round. Even more fun, Neil Paine at Basketball Reference ran Monte Carlo Simulation of the tournament 10,000 times using Pomeroy’s values and posted the very interesting results. Jeff Sagarin’s list uses scoring margin and a clever use of the Elo rating system (originally designed to rank chess players) to come up with his list of things to pick. Naturally, Nate Silver can’t resist weighing in with his method of making picks, which basically does for March Madness what Five Thirty Eight did for electoral math. His system, much like his polling methodology, is a weighted aggregation of different sources like Ken Pomeroy and Sagarin’s ranking plugged in with other factors that Silver thinks are important like geography, player ranking, and pre-season ranking. The sources he pulls from are exhaustive and smart while his methodology is well-reasoned. That said, it’s worth mentioning that a dumb “wisdom of the crowds” type list, such as ESPN’s national bracket (an average of all individual brackets) tends to outperform the majority of individual brackets.

Now, here’s the question: are you trying to predict the winner of games or are you trying to win a pool? These are not the same thing and it’s important to make the distinction. The national bracket, as I mentioned, usually gets a lot of the answers right. For the big questions, common sense is usually close enough. You want to know who has the best chance of winning the NCAA? Ohio State.  Pretty much every system, rankings, and analytics have Ohio State as the best team in the country. I happen to think this as well. I also think that the four number one seeds have the best chance of making it to the Final Four. Lots of folks agree with me and lots of analytics back it up.

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Behind the Numbers: Focus and Balance

Posted by KCarpenter on March 9th, 2011

Kellen Carpenter is an RTC contributor.

What’s the ideal team look like? Is it five equally skilled players who share the ball and can all defend and score? A super deep team with ten guys who could all start? Does the ideal team have an alpha dog and a clear hierarchy? A dynamic duo? Maybe, a Big Three? If it’s one superstar and role-players, where does the superstar play? Is he a guard or a forward? One of the things that I enjoy most about basketball is that there is no clear consensus on any of these ideas. There is no ideal. Instead, we have a multitude of competing styles on offense and defense, each individual style perfectly capable of winning a championship.

How Important is the Bench to a Contender?

In 2010, the national championship went to a Duke team with a Big Three approach, focused on the perimeter. In 2009, North Carolina won by balancing a primary post option in Tyler Hansbrough with three skilled guards. The Kansas and Florida championship teams leaned heavily on their skilled big men. The point is that there are successful precedents for most styles, independent of focus and number of focal players. Now, despite these precedents, there is a common idea that a limited number of focal players makes a team more vulnerable. Intuitively, this makes sense: it’s easier to stop one player than many. Likewise, if a team has a single focus, like post scoring, it seems intuitively easier to stop that without having to worry about other threats, like perimeter shooting.

These ideas make sense, but I’m not sure they hold up. Teams with a single focal point, a single powerful talent, have been successful. Notably, in 2003, the indisputably Carmelo Anthony-led Syracuse team won it all. In a similar vein, the 2010 Blue Devils’ lack of reliable post scoring didn’t seem to impede their success. So, at least anecdotally, a lack of balance doesn’t seem to be terribly lethal to a team’s hopes for a championship. But, what about the question of depth? Doesn’t having a deeper team help a team win?

Not necessarily. If we look at bench minutes percentage, a measure of how many total minutes were played by non-starters, we can get at least some measure of a team’s depth. Generally speaking, a deep team will have more minutes played by guys on the bench. Last year, the two teams that played in the final, Duke and Butler, finished 1st and 12th in Ken Pomeroy’s Pythagorean rankings. Looking at these contenders and the ten teams that finished ranked between them, there was only a single team that was above the median in bench minutes percentage. Every other team was in the bottom half of bench usage, while many, notably Duke and Butler, were in the bottom fifth. Somewhat surprisingly, the one top team that used the bench at an above average rate was Brigham Young, a team that was primarily known for the singular talents of Jimmer Freddette.

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Behind the Numbers: Defense and the Individual

Posted by KCarpenter on March 2nd, 2011

Kellen Carpenter is an RTC contributor.

This is a story about Kenneth Faried and Morehead State. I know that it’s March now, and that Morehead State, who went 13-5 in the Ohio Valley Conference, has only the slightest chances of making a tournament splash. We have all March to talk about the contenders and the Big Dance. For now, let’s talk about Kenneth Faried and Morehead State. Actually, first let’s set the stage.

Faried is BTN's National Defensive POY

We are very good at measuring offense in basketball. We have a good sense of what is valuable and how much impact a player can make on the offensive end. The box score stats provide enough of a jumping-off point that a few bits of mathematical transformation can paint a pretty clear picture of a team or player’s impact on the offensive end. We’re talking about offensive efficiency, as good a tool as we have in college basketball. It’s so good, in fact, that we like to cheat and use opponent offensive efficiency to measure defensive efficiency, which is a pretty clever little trick. By measuring how opponents perform against a given team on average, we have some measure of that team’s defensive abilities.

The operative word here, however, is “team.” While we can tell how well a team performs by measuring their opponents foibles on offense, how do we assign individual credit? Not every player defends equally, and while I wouldn’t argue that defense isn’t a team effort, surely some players have a clear measurable defensive value over others. Defensive efficiency tells us very little about this.

Of course, maybe I was getting ahead of myself by plunging into advanced stats before just checking out the box score. Steals, blocks and defensive rebounds are all individually counted categories that suggest defensive aptitude, and indeed, after converting these categories into their tempo-free counterparts, we have a pretty good suggestion of players with specific defensive abilities. There are, however, problems with these categories. A block where an opponent retains possession really doesn’t do much good, yet those blocks are counted just the same as those that trigger fast-break opportunities. Steals generate extra possessions, but failed gambles for steals can lead to high-percentage shots. The example with steals highlights the bigger problem of what isn’t captured by the box score, namely, how good a player is at making the man he is guarding miss shots. Now, manual defensive charting and a thorough parsing of play-by-play data might be able to produce a pretty good individual opponent field goal percentage ranking, but so far, accurate and comprehensive data of this sort is just not available to us.

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Behind the Numbers: Cliches and Champions

Posted by KCarpenter on February 25th, 2011

I like cliches, because they give me something to do. The proverbs of the sporting world and the received aphoristic wisdom of our hallowed forefathers are well-known and often taken for granted. They are also, fortunately, not too hard to test or analyze. I’m a fairly agreeable guy, but I must say, few things give me as much joy as being contrary in the face of stupid cliches. It’s an easy thing to do in the blogosphere, equivalent to shooting fish in a barrel, and there are many out there who are better gunslingers than I. But, for now, let’s joyously take aim at the hoariest one of all: “Defense wins championships.”

Great Sign, But Does It Win Championships?

Obviously, playing some defense is necessary to win anything. No one is arguing with that. But what the phrase really seems to mean is that teams with excellent defenses are the ones that win the big one. More than that, the phrase implies that defense, above offense, is the thing that separates the great teams from the good ones. Like so many things, it seems like our little proverb has things half right. In college basketball, the national champions have all been excellent defensive teams. The worst defenses to have won the title since 2003 are Syracuse (in 2003), or arguably, North Carolina in 2009 and even then, both of these teams had defenses that ranked in the top twenty in terms of defensive efficiency. Teams with bad defenses don’t win championships. If we want to take our proverb only this far, we can be happy.

The suggestion that quality defense is more important than quality offense is where the trouble starts. While every title-winning team since 2003 has had a quality defense, they have also all had quality offenses. The worst offense of any of these teams also belonged to that 2003 Syracuse team and it was, by Ken Pomeroy’s reckoning, the eleventh best in the country. So, it seems that we could, if we wanted, reasonably compromise and say, “Offense and defense win championships,” but that is ridiculously banal, and reasonable compromise is kind of boring. If you want to pick only one, offense is what wins championships.

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Behind the Numbers: The Other Guys of the Year

Posted by KCarpenter on February 16th, 2011

Kellen Carpenter is an RTC contributor.

The Player of the Year race in college basketball is an interesting and bizarre thing. The most talented player is rarely selected, and the winner is seldom a National Champion. I don’t want to go so far as to say the race is a popularity contest, but it’s something akin to one. Instead of picking the best player, the voters like to pick the most emblematic player, or failing that, the most interesting. Oh, and that player has to almost inevitably be a bit of a ball hog. Evan Turner was not the best basketball player in the country last year, as fans of the Philadelphia 76ers know all too well, but he was a skilled-enough, multi-talented player on a pedigreed team that won a lot of games. With that logic in mind, it’s pretty safe to pencil in Jimmer Freddete, Jared Sullinger, Derrick Williams, Kemba Walker or Nolan Smith as the front-runners of that race. This was true in December, as well. I don’t want to say that the national Player of the Year race is dead, just that it’s perpetually unsurprising, even if the final result does have that extra spice of arbitrariness thrown in for good measure.

Walker Headlines a Strong NPOY Group of Candidates

So instead of breaking down the Player of the Year race and debating just how good, on the scale of really good to incredibly good all those familiar faces are, I thought we could take some time to show some love to some mostly unfamiliar faces who are having extraordinary and superlative seasons of their own. Maybe they don’t play a great all-around game, maybe their teams don’t win, and maybe some of them aren’t good so much as weird, but let’s celebrate them all anyway. We need a name for this party, though, so let’s call it the Other Guys of the Year Awards, dig into the depths of Ken Pomeroy’s stats tables, and hand out some imaginary statuettes.

The first awards go to a pair of players who play for the same team in the Big South. The Iron Man Award goes to Khalid Mutakabbir of Presbyterian who has played 96.1% of all available minutes, a greater percentage than any other player in Division I. Mutakabbir has used those minutes well, shooting a high percentage from the field, and a very impressive 51.7% from beyond the three-point line. The Ultimate Ball-Hog Award goes to Mutakabbir’s teammate, Al’Lonzo Coleman, who somehow comes off the bench, yet uses 36.3% of all possessions, more than The Jimmer himself. While Coleman is undoubtedly president of the Ball-Hog Club, let’s give some special recognition to the other players who, despite living outside the national limelight, have managed to dominate the ball more than Mr. Fredette: Special thanks to Keion Bell of Pepperdine, Anatoly Bose of Nicholls State, Brandon Bowdry of Eastern Michigan, Adrian Oliver of San Jose State, and Will Pratt from Northwestern State. You have all out-Jimmered the Jimmer, except for, you know, the winning games thing.

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