# Category Archives: ncaa

## Predicting the future, by analogy

Many times before, I’ve posted network diagrams which I suggest highlight objective similarities between athletes, according only to their statistical production. I’ve also noted that one of the most common discussions, especially around the draft and its aftermath, is that which attempts to identify which current or past professional player is most similar to which draftee. This is done, I believe, to convey some idea of playing style, but also, I think, to convey some idea of an individual’s potential. If a collegiate or recent draft pick gets compared to Michael Jordan instead of Zan Tabak, it means that the comparer thinks the rookie is more of a scoring wing player than a non-scoring center type, and that he has the potential to be a very good player in the NBA, rather than a very good player in Europe.

Thus, I thought it would be useful to do this same sort of comparison, but statistically, rather than subjectively. The main problem I encountered is that one cannot just add a college player’s statistics to a database of pros, match them, and expect the results to be valid. A player who scores 28 ppg in college could turn out to be a prolific scorer in the NBA, but he may also turn out to be Adam Morrison. Even comparisons of two players’ statistics across NCAA teams, I would submit, is shaky, given that college teams are so variable in terms of playing styles and abilities. Nevertheless, that it what I have chosen to do: Compare the collegiate statistical profile’s of some of this year’s draftees to those of other recent draftees, and suggest the inference, by analogy, that their professional careers will be similar to those whom their college careers match. I understand that this is fraught with tenuous connections and weak connections, but given my personal data limitations and relative lack of patience and time, this is what I’ve come up with:

Statistical Proximity of Selected NCAA Basketball Players [pdf]

Incidentally, player vertices are scaled according to their per-game MEV (Model-Estimated Value-similar to the calculation for BoxScores), and colors are according to the Playing Style Trichotomy outlined here. I find it interesting that the algorithm matches Michael Beasley with Kevin Durant, who just had a ROY season. Derrick Rose isn’t directly connected to anyone spectacular, though he is only two degrees of separation from Chris Paul, which is good company. OJ Mayo is tied to Ben Gordon, who is off to a promising start in the NBA, and Rodney Stuckey is most closely matched to Dwyane Wade (perhaps the Pistons used similar methodology in making their pick). Anyway, I’m sure many of you will gain greater insight from the graphic than my own descriptions, so please fill me in with a comment.

## How did I do?

Yesterday, I linked to projections I’d made for the Championship game between Memphis and Kansas. I predicted a Kansas win, 71.07 to 70.74. On the plus side, these projections round to a tie, which we had going in to the first overtime. Also on the plus side the projected sum of points (141.81) is very close to the actual sum of points (143), and I did have Kansas winning. On the negative side, I needed an overtime (which I did not actually predict) to get point totals anywhere near my prediction, and I had it ending a little closer than it was. We did have a buzzer beater, though, which I am willing to say I predicted. Actually, having reasons to root for both teams, I was mostly pulling hard for a final score of 71 to 70 or something like that, and I’m not too disappointed. Here is how I did on the rest of my projections… none to well, unsuprisingly:

http://spreadsheets.google.com/pub?key=pjtolzxemBV6kYuIHIE9ZGA

## The long and winding road… to a championship

Regarding tonight’s game: The road to the NCAA Championship, brought to you by ESPN. For the record, I have Kansas winning 71.07 to 70.74. I suppose this is just a statistical way of saying that it’s a toss-up, but I’m sticking to it. Enjoy the game!

## Put together enough lines and shapes, and eventually they’ll point to a winner

By way of a NCAA tournament championship game preview, I present to you Memphis vs. Kansas, head-to-head, sparkline edition, courtesy of our friends at ESPN. If you like that, you may enjoy some of the designer’s other work at https://arbitrarian.wordpress.com.

## Basking in adulation or drowning in hate?

Which better applies to your favorite team? Using NCAA basketball data collected by Facebook, I’ve thrown together a scatterplot of the teams which elicit most passion (measured by number of opinions expressed), contrasted with the favorability with which each team is viewed. Unsurprisingly, several of the larger state schools rank among the top in terms of number of opinions expressed, and just as obviously, Duke elicits the greatest number of opinions. Princeton, Yale and Harvard all rank toward the bottom in terms of favorability, although this is likely not due to their fearsome basketball reputations. I have to feel sorry for the Bethune-Cookman Wildcats, who appear to have a small, but hateful, following. The most beloved team appears to be the Wake Forest Demon Deacons, followed closely by St. John’s Red Storm. Between Wake, NC State (also well-liked), UNC and Duke, North Carolina is well represented at the extremes. Enough prologue, Here’s the graphic:

And, for those interested, here is a listing of teams by percent favorable opinions:

NCAA Men’s Basketball Favorability

I would love to see crosstabs for the fans/haters. If one wanted to operationalize “greatest rivalry” I think this would be an excellent way to do so.