Oklahoma City won game 5 last night and so won the series 4-1. Here’s a Synergy treemap breakdown of the series. Continue reading “Visual Summary of Thunder-Lakers Series”
Got to have a disclaimer on things like this, so here it is:
The following ratings are for informational and/or entertainment purposes only. The creator of said ratings does not (necessarily) endorse using these ratings as sole criteria for MVP determination. Usage of these ratings in MVP discussions on the internet or twitter entails certain risks, including, but not limited to, people telling you to watch the games with your eyes, and other people calling you crazy for suggesting Matt Bonner or Vince Carter are more valuable than you might realize. People may unfollow you. These ratings are valid for 2012 only and are subject to change in future seasons of basketball.
Without further adieu, I’ve split the ratings into two sets. The first set is for players with >2500 possessions, and the second is for players having between 1000 and 2500 possessions. The reason for splitting it up this way is simply to acknowledge that we should have more confidence in the ratings for players with a larger sample size, especially in this shortened season. The ratings are sorted in descending order by the A4PM rating (make sure to read that article if you don’t know what A4PM means). The column VARP is Value Above Replacement Player, calculated as follows:
The value 2.0 was used as the replacement level, since it represented approximately the value of the 15th %-ile of players with >1000 possessions. Continue reading “A4PM Ratings for 2012 (Not Explicitly An MVP List!)”
Kyrie Irving is going to win Rookie of the Year, and he would get my vote, even though as you’ll see it’s not quite that clear cut from an advanced stats perspective. Here, we’ll look at how this year’s freshman class performed in three of my homegrown statistical metrics: ezPM, A4PM, and PSAMS.
This post should be thought of more as a tool than a prediction, although I do give predictions at the end of each section. The tables and treemaps below show “expected scoring” for each series. Expected scoring is simply defined as the average rate (%-total plays) and efficiency (PPP) between the offense of one team vs. the defense of the other team (and vice-versa). The data comes from Synergy, of course. Continue reading “Advanced NBA Playoff Previews and Predictions: Round 1”
Of course, not. But…
Jayson Williams, freshly released from prison, was quite a prolific offensive rebounder. If you don’t believe me, look at this table of the top offensive rebounding seasons (by ORB%) in the 3-pt era: Continue reading “Does Jayson Williams Belong in the Hall of Fame?”
Some more stats to throw at you today using my new distance metric, which judges scoring based on both efficiency (measured by TS% ) and volume (measured by USG%).
Here are the rookies in 2012 with greater than 300 FGA attempted. Recall that 1.0 is the greatest DIST a player can have, 0 is what an average player would have, and -1 would be very bad. I’ve also standardized the rating according to how rookies peform. That’s given in the STD column. You can see that Kyrie Irving has been a very, very good scorer. He is 2.5 standard deviations above an average rookie. Klay Thompson (yes!) and Isaiah Thomas have also been quite good. Continue reading “Top Scoring Rookies in the 3-Point Era”
I created a Tableau visualization of all 10,000+ TS-USG data points, which includes all player-seasons since 1980 with greater than 100 FGA in a season. Allow me to give you a virtual tour, before you go off and play with the data on your own (be careful!).
Think of the big blob of data points roughly as a clock. Let’s go around counter-clockwise. When you explore the Tableau viz, you can zoom in on data points, and click on individual points to see more details, such as the year, team, and age of the player. Continue reading “Exploring the Usage-Efficiency Landscape A Bit More”
An age-old question — see what I did there? — among APBRmetricians is trying to understand how aging affects players. Consider this post my first contribution to the discussion.
I calculated the distance metric that I introduced in a recent post for the 10,000 or so player seasons since the 3-pt shot was instituted. I then divided these seasons into four groups by age, as follows:
- “very young” (18-21)
- “young” (22-25)
- “prime” (26-29)
- “old” (30+)
That title ought to have gotten your attention. 🙂
In an effort to look deeper into the (hypothesized) tradeoff between usage and shooting efficiency, I went to basketball-reference and compiled a list of every player-season of >100 FGA since the 3-pt shot came into effect. There are roughly 1800 unique players in the list and a little over 10,000 seasons (each represented by a row of data). I also captured the player’s age, which you’ll see in the plots that follow.
ts.lme<-lmer(TS.~USG. + Age | Player,data=usage_big,weights=FGA)
I was going to call this the convex hull of the usage-efficiency relationship, but decided that would truly scare away everyone who was actually going to be interested in this.
You have probably heard of the usage-efficiency relationship. It’s debatable in some circles, and taken for granted in others. The idea is that the more a player shoots, in general, the less efficient he becomes. Ideally, we would just regress TS% (or some other measure of efficiency) onto USG% (or some other measure of volume) and see a nice, tight (well-correlated) and linear relationship. Like this one for 2012 (data points are for players with greater than 500 possessions):