by Ken Pomeroy on Wednesday, November 12, 2014
Who’s going to be the best three-point shooter in the land this season? If you can predict that, you can call yourself very good at predicting things although I would call you very lucky. I don’t have any special powers here, but I thought it might be fun to put together five shooters that would shoot very well this season. So well that it would be difficult to come up with another five-man team that would beat these guys.
True to my hermit-like nature, I didn’t consult a bunch of coaches or scouts to come up with this list. I used each player’s shooting numbers over the past two seasons to predict their three-point percentage this season, all from the comfort of my basement. The numbers I used were three-point, two-point, and free throws made and attempted. The three-point and free throw numbers are indicative of a player’s shooting touch. The two-point numbers probably pick up on a mix of shooting touch and size, or at least ability to get off a shot.
So did you make team? That’s a dumb question, because chances are you don’t play college basketball and you probably suck at three-point shooting. But these five guys do play and they can’t stop and won’t stop making shots.
by Ken Pomeroy on Monday, November 10, 2014
The anticipation for preseason ratings is an interesting thing. People like to talk about the projections, poke holes in them, figure out why their team is underrated. But obviously, no team is bound by the shackles of its preseason forecast. Oregon State is predicted to finish last in the Pac-12 by most robots (and humans, for that matter), but nothing is stopping the Beavers from running the table in conference play.
I guess if you want to get technical, a lack of talent might be a significant obstacle for them. But the robots will not be. If Oregon State wins its first four conference games, the robots are not going to hire Shane Stant to visit the Beavers’ locker room. In that sense, the ratings are irrelevant.
However, there is good reason to have some interest in preseason projections. For most programs, the forecasts are a reasonable estimate of what neighborhood a team will reside in this season. There were four systems that made comprehensive preseason forecasts last season. Here’s the average error in forecasting regular-season conference wins for each of them…
by Ken Pomeroy on Tuesday, November 4, 2014
Which two teams have gone the longest without losing on the same day? You might not care but regardless, I feel I have an obligation to tell you. Your indifference only motivates me to continue to track this fact with the intensity of 1000 red suns. If you’re new here, you can check out past editions of this post like the one before last season and the one that started it all in November 2012. After reading those, if you don’t care, then you may want to see a doctor and make sure your heart is functioning, because it might not be.
As it happens, the answer to this question is the same as it was last year at this time. The last time Ohio State and Kansas lost on the same day was February 19, 2005. I don’t know how rare it is for the top streak to survive a full season since this is only the third year I’ve done this, but I expect it’s rare. I can say that with confidence because the reigning pair now has nearly a two-year advantage over the next-longest streak. I can also say that with confidence because of the ten-longest streaks entering last season, just two survived. Here is that list from a year ago…
by Ken Pomeroy on Monday, November 3, 2014
With the release of the first AP poll last Friday, it’s time for a refresher on the historic value of those rankings. The preseason AP poll is not going to tell you exactly how the season will play out, but given the poll has a long track record, we can use history to tell us the chances of a team ranked in a specific position getting a particular seed in the NCAA tournament.
If you’re familiar with my work, you know by now my support for the preseason poll, and this will seem repetitive. But the data below is updated to include last season, so this is not a complete waste of your time.
Last year, two teams ranked in the preseason failed to make the tournament: #17 Marquette and #21 Notre Dame. That’s better than normal. Since the poll expanded to 25 teams in 1990, an average of 3.7 ranked teams per year have missed the field altogether. Kentucky’s appearance in the NCAA tournament kept a perfect streak alive for the preseason’s #1 team. Every #1 team has made the field since it was expanded to 64 teams in 1984-85.
by Ken Pomeroy on Tuesday, October 28, 2014
By now, you’ve noticed the preseason ratings have been posted. Thanks to all that have stopped by the past 24 hours. My server thought it was March on Sunday night. (h/t to Matt Norlander for the tweet that generated the traffic. I usually enjoy flipping the switch and watching twitter spread the word organically over the course of a few hours, but since Norlander spilled the beans approximately five minutes after the site turned over, I got an immediate firehose of traffic.)
I’ve discussed the formula in some detail in previous seasons and it hasn’t changed much in the five years I’ve been doing this. Here are some semi-random thoughts on them.
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.
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%.
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.
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.
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…