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Most recent entries

  • Introducing Court Intelligence
  • Studying whether to foul when tied, Part 3
  • Studying whether to foul when tied, Part 2
  • Studying whether to foul when tied, Part 1
  • Offensive rebounding data dump
  • Conference history pages
  • Golf win probability model description
  • A review of 2002
  • BeeJay Anya plays 110 pounds less than he’s listed
  • Your 2014 kPOY: Russ Smith
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    Introducing Court Intelligence

    by Ken Pomeroy on Monday, September 29, 2014


    In recent months, I have received numerous emails from coaches inquiring if there is a tool available that will track advanced stats for their teams. In the past, I have responded with ignorance since I haven’t had the need to do this myself. But those days are over and I can now respond with knowledge.

    The Court Intelligence app powered by kenpom.com is now available for your iPad and allows you to track all the action during a game (or by reviewing video). Give it to an eager assistant coach or volunteer and you’ll have a gold mine of data at game’s end. The app can track various tempo-free stats associated with any player combination (individual through five-man combination) and aggregate them over a whole season. If you have the time, you can track opponents games as well to get an idea of what is working for them.

    Read more...

    Studying whether to foul when tied, Part 3

    by Ken Pomeroy on Thursday, July 24, 2014


    This is the third and final part of my series of whether to foul when the game is tied and the shot clock is off. If you missed part 1 and part 2, don’t worry, you’re joining us just in time. The payoff was this guide to when the strategy of fouling is justified.

    Maximum free throw percentage to implement fouling strategy
    Win probability     FT% threshold  
    Pre-game    OT     1-and-1  2-shot
      .900    .756       58.2    39.9
      .800    .671       62.2    44.7
      .700    .607       65.4    48.8
      .600    .552       68.4    52.9
      .500    .500       71.4    57.3
      .400    .448       74.4    62.0
      .300    .393       77.9    67.6
      .200    .329       82.4    74.8
      .100    .244       88.2    84.2
    
    

    For example, if we estimated that a team had a 40% chance of beating its opponent before the game started, that team would be justified in fouling a free throw shooter worse than 74.4%.

     

    Read more...

    Studying whether to foul when tied, Part 2

    by Ken Pomeroy on Saturday, July 19, 2014


    In Part 1, I went over some of the relevant win probabilities to consider for the strategy of fouling when the game is tied and the shot clock is off. If one assumes that any missed free throw is rebounded by the defense, then the strategy of fouling would be recommended in the majority of situations. Assuming perfection is a bad way to evaluate strategies, though. This is why some analyses of fouling up 3 tends to show a large advantage for fouling when history suggests that advantage is small (or none at all). Or that attempting to get the 2-for-1 at the end of a half is beneficial, but not as much as one might think.

    Factoring in the possibility of an offensive rebound to this analysis makes it somewhat more complicated, but it’s necessary to determine the merits of fouling. My examples will all use the single bonus situation. Not surprisingly, the math is much more favorable for fouling when the offense is in the single bonus since the possibility of making zero free throws increases.

    Read more...

    Studying whether to foul when tied, Part 1

    by Ken Pomeroy on Sunday, July 13, 2014


    Late in the 1983 national championship game, heavily-favored Houston held the ball with a little over a minute remaining in a tie game. In the pre-shot clock era, Houston had the opportunity to hold for the last shot. N.C. State head coach Jim Valvano implemented what would be considered a controversial strategy today, ordering Dereck Whittenburg to foul Alvin Franklin with 1:05 remaining, instead of playing defense and hoping to get to overtime.

    Neither member of the CBS broadcast team, Gary Bender or Billy Packer, criticized the idea. Actually, the normally disagreeable Packer was fully supportive of the strategy. And he should have been. Franklin was a 63 percent shooter from the line, and—spoiler alert— I’ll show the math that supports that Valvano gave his team a better chance of winning by giving the foul, assuming Franklin truly had a 63 percent chance to make his free throws.

    Read more...

    Offensive rebounding data dump

    by Ken Pomeroy on Wednesday, July 2, 2014


    If it’s the middle of summer and you’re obsessing about trends in offensive rebounding, then you’re either me or some sort of lunatic. I was initially concerned about a very specific aspect of offensive rebounding for an upcoming feature, but along the way I decided to look at the different things related to offensive rebounding that one can mine from play-by-play data, and that led me to what you are about to see.

    This isn’t going to be the most glamorous piece of analysis, but if there was a good time for an offensive rebounding data dump, this is it. Now for some facts regarding those second chances…

    Read more...

    Conference history pages

    by Ken Pomeroy on Monday, May 19, 2014


    You can now find a conference history page linked on each conference’s page next to the years list. While we tend to speak about the state of the game in terms of averages across Division-I, each league is an ecosystem in itself, with its own long-term norms distinct from the rest of the college hoops world. The Southland plays fast, the Big Ten plays slow, the Summit makes a bunch of shots, the SWAC doesn’t, and the SEC is not the place for those that fear rejection.

    Read more...

    Golf win probability model description

    by Ken Pomeroy on Saturday, May 10, 2014


    Let’s face it, golf is not the most exciting spectator sport. Many find greater enjoyment in painting their ceiling or studying Latin. However, behind the slow pace of a golf tournament is a chaotic system, where about 150 players of various talent are simultaneously competing for a victory. But four rounds of golf is often not enough to separate the best golfers in the field from everyone else.

    In fact, no matter the skill of the golfer, the winner will have to play better than his long-term average. Thus, the typical professional golf tournament is a tribute to randomness. One with expert knowledge could go an entire year without successfully picking a tournament winner and not feel too bad about it. Who is going to play over his head this week? That is the question that must be answered to predict a winner and no one can know that answer with much certainty.

    In order to understand this messed-up world, I’ve been trying to develop a credible win probability model for golf tournaments for a while. This is not something that will solve one of sport’s great mysteries, but perhaps it will make more sense of the wacky world of professional golf, where each tournament contains 2.5 times the entries of the NCAA tournament with significantly more parity than college hoops.

    Read more...

    A review of 2002

    by Ken Pomeroy on Monday, April 28, 2014


    Ever want to relive the 2002 season? Now you can, as tempo-free data for the 2002 season has been posted. Thanks a bunch to Josh Steele for filling in the holes of my data set to make this possible. Here are five interesting things I noticed about that season, but I’m sure you can find more!

    5) It was not that long ago, but it was a different game. The average game in ‘02 contained 69.5 possessions for each team. That figure would put a team around 50th in adjusted tempo in 2014. Kansas, for one, played just one game that had fewer than 70 possessions. The average ACC game had 74.2 possessions. This season, it had 61.8, which represents an 18 percent decline. There was also just more going on in college basketball back then. More offensive rebounds and turnovers, especially. Perhaps this is bordering on “the game was better in my day” talk. I’m not saying the game was better, but it was more fun. The data backs this up. (Except for blocked shots. There are more blocked shots now.)

    4) The SEC was the best conference in America. This wasn’t the last time it would be rated as the top conference by my system - it’s happened as recently as 2006 - but I suspect the ACC got more notoriety by having two one-seeds. However, the bottom of the ACC was clearly worse than the bottom of the SEC. It was the season UNC lost 20 games and Florida State and Clemson were equally bad. The SEC on the other hand put all 12 of its teams in the top 100. That balanced hindered the top teams from putting up a great record and only one SEC team lost fewer than six conference games.

    Read more...

    BeeJay Anya plays 110 pounds less than he’s listed

    by Ken Pomeroy on Monday, April 21, 2014


    Whose listed weight is the most misleading? You didn’t know you wanted to know that and I didn’t either, until I started trying to predict a player’s weight based on other information. And the only reason I did that was because sometimes a player’s weight is not revealed. Mostly in cases of players on Utah Valley’s roster as the Wolverines are the lone team in college hoops that treats its players’ weights like Bill Belichick treats his players’ injury information.

    You might think it is really dumb to be bothered by such minutiae, and that’s fine, but you are not the intended audience of this site. Go to some other place and read “normal” things about “people” and stuff. In the real world, when one entity out of 351 refuses to do something the other 350 are doing, it can create a headache. I mean, that “effective weight” stat people have been demanding is not possible because of the Wolverines.

    But there is no need to let Utah Valley hold us back from having a complete data set of players’ weights. One can look at available players’ weights and all of their accumulated statistics and figure out what matters when predicting what a player’s listed weight will be.  And this produces a pretty decent estimate. By pretty decent, I mean a standard error of 13.5 pounds for the 2014 season by using data from the 2007 through 2013 seasons to build the model.

    Read more...

    Your 2014 kPOY: Russ Smith

    by Ken Pomeroy on Tuesday, April 8, 2014


    Congratulations to the winner of the 2014 kenpom.com player of the year award, Russ Smith of Louisville. Smith earned his second consecutive kPOY by posting a 114 offensive rating while using 31 percent of the Cardinals’ possessions when he was on the floor. That was the fourth-highest offensive rating among players using at least 30 percent of their team’s possessions. In addition, he recorded 3.96 steals per 100 possessions, which ranked 32nd in the nation, while playing 29 minutes per game for the nation’s fourth-ranked defense. In 2014, no player combined offensive production and defensive impact like Russ Smith. Russ joins past winners Jared Sullinger (2011), Draymond Green (2012), and himself (2013).

    Read more...

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