Okay, so I finally cracked and decided to do something with the SportsVu data over at NBA.com. I'm starting out small, so as to not waste my efforts on an extremely small sample of data. Today, I decided to look at play-making ability. This isn't a new concept, and is usually done using Assist:Turnover ratios. However, with SportsVu at or disposal, we can take it a little further.
The first step was to determine what percentage of a player's Touches ended in a Turnover (bad) or a Potential Assist (good). This was done by simply dividing total Turnovers/Potential-Assists, by total Touches. Next, I needed to establish the "value" of a Turnover and Potential Assist. A Turnover is is usually counted as -1, since you lost your team a possession, and the average team scores about 1-point-per-possession.
The Potential Assist was a bit trickier, since I don't have access to enough data to come up with an exact value. I decided to start with the Win Score value of an assist (+.5). As of today, 50% of all Potential Assists become Assists, so I divided .5 by 2, to get a value of +.25 for a Potential Assist. Not perfect, but it should suffice for now.I weighted Turnovers and Potential-Assists as described above, added them together, and standardized. Below is a table ranking the top 30 play-makers in the NBA.PAPT% = Potential Assist per Touch Percentage
TPT% = Turnover per Touch Percentage
PME = Play-Making-Efficiency
- To qualify for the table, a player must rank at least average in terms of minutes per game, and touches per game. PME is pace and minute adjusted
- Wade, Harden, Westbrook, and Durant all ranked below average.
- Questions will be answered in the comments, and hopefully I can clean the values/methodology up as the season progresses.
I'm completely swamped with work from my actual job (lab-chemist), and kind of in-between major projects for the site. So, I thought I would cobble together a fun little piece ranking the most "clutch" NBA players from the 2013 season. Clutch, being defined as the last 5 minutes of a game, with neither team ahead or behind by more then 5 points.
We all know about the clutch stats available at 82games and NBA.com, which provide a great record of everything a player does in crunch-time. Unfortunately, neither site really attempts to rank overall clutch play, so I decided to take a crack at it.
I'll be calling this the HDR (Hoop-Don-Rating), which is essentially Alternate Win Score (the best linear-boxscore metric) mixed with Defensive Rating (a defensive adjustment).
The final HDR rating is per-100 possessions (pace adjusted).
To qualify for the top 30, players had to play more then 75 total "clutch minutes" (basically the average or above).
DRTG = Defensive Rating
AWS48 = Alternate Win Score per 48 minutes
HDR per 100 = Hoop-Don-Score adjusted for pace
- LeBron James is the "clutchest" player in the NBA. In fact, the Heat as a team were pretty clutch last year, as evidenced by Chris Bosh, Ray Allen, and Dwyane Wade ranking in the top 30.
- To be fair, multiple teams had multiple players appear in the top 30 (CHI, SAS, MEM, NYK).
- Larry Sanders was apparently #2 in clutch-effectiveness. For the record, Carmelo ranked #57.
- Chris Paul was the 2nd best clutch player via AWS48, but his negative Defensive Rating pulled him down to #17. Sorry CP3.
That's all folks. Questions about methodology will be answered in the comments.
This is the second part of my skilled rebounding study, this time with a focus on Defensive Rebounding. The full methodology is explained here, on the Offensive Rebounding article I last published. I know, I'm lazy, I don't want to go through it again.
The only change was converting the expected Offensive Rebounding Percentages into expected Defensive Rebounding Percentages, which was done by subtracting the ORB% from 100, to get DRB%. The breakdown is below.
Remember, the process is actual rebounds (DRR) minus expected rebounds (XDRR), equals skilled rebounding (XDRD).
DRR=Defensive Rebounding Rate
XDRR=Expected Defensive Rebounding Rate
XDRD=Expected Defensive Rebounding Difference
- Normal DRR had a +8.51% correlation with wins in 2013. The calculated XDRR had a -1.61% correlation, and the "skilled" XDRD had a +15.4% correlation.
- The Thunder, Heat, Bucks, and Blazers were all below average in terms of traditional DRR, but based on where their opponents shot from, they were actually above average rebounding teams.
- The Wizards, Bulls, Wolves, and Clippers were all above average in terms of traditional DRR, but based on where their opponents shot from, they were actually below average rebounding teams.
That's all for now. I'm working on some cool ideas for rating a players "value" to his team, as well as messing around with the new SportsVu stats at NBA.com. I'll tabulate the "skilled" Total Rebounding Rates for 2012-2013, and post them on a tab at the left, along with the previous Offensive/Defensive Rebounding components.
This post, and hopefully future ones, will focus on measuring "skilled" rebounding at the team level. This will be done by isolating and removing the effect that shot locations have on rebounding rates, which ideally, should leave us with a measure of each team's rebounding "skill".
The first step in this process was to pull shot location data for each team, from Hoopdata. I then calculated the percentage of each team's missed shots for each of the 6 major shot locations. This includes misses for free-throws, which were tabulated using team FT% and the .44 multiplier. An example of this data is shown below.
Next, I determined the league average offensive rebounding rate for each major shot location shown above. This was done by looking at the above Grantland chart, this Count the Basket chart, and using NBAWOWY to look at rebound rates of missed 3-pointers and Free-Throws. The tentative values are displayed below.
The expected Offensive Rebounding Rate was multiplied by the percentage of each team's misses by shot location, summed, and standardized. I then subtracted this number from each team's actual Offensive Rebounding Rate (standardized), to arrive at a "skilled" rebounding values.
This process is essentially Actual Rebounds (ORR) minus Expected Rebounds (XORR), equals Skilled Rebounding (XORD).
ORR=Offensive Rebounding Rate
XORR=Expected Offensive Rebounding Rate
XORD=Expected Offensive Rebounding Difference
- Normal ORR had a -.487% correlation with wins in the 2013 season. The calculated XORR had a -3.79% correlation. The "skilled" XORD had a +1.48% correlation.
- The Nuggets were the best Offensive Rebounding team in the league during the 2013 Regular Season, but based on where they took their shots from, they were actually a below-average rebounding team.
- Atlanta and Miami are two teams who were ranked quite low in traditional Offensive Rebounding Rate (ORR), but actually seemed to rebound at a league-average rate, based on where they took their shots.
That's all for now. I plan on doing Defensive Rebounding once I monkey around with the weights a little more, just to make the results as tight as possible.
At long last, the 2013-2014 NBA season is upon us. There will be no Head to Head matchup this week, as I was furiously cobbling together a respectable projection system. Here's the gist of it.
I used a weighted blend of PER, Win Shares, XRAPM, Wins Produced, and ASPM. Basically, it was something similar to the Blend Player Ratings shown at the left, but with different weights, more suited to predicting wins then explaining them. I gave all rookies the league average rating (0), unless they were projected to play more then 1,000 minutes, in which case I determined some sort of rating for them. I'm not saying how, because it wasn't very scientific, but it seemed to be in line with the majority of rookie projections, so whatever.
Projected Eastern Conference Standings
Projected Western Conference Standings
I scaled the ratings so every team falls into the 20-60 win range. Its just statistically prudent to be there, even if a couple teams will likely fall outside those bounds.
Based on the other projections I've seen over at APBR and BoxScoreGeeks, a few things jump out from my model.
I project the Wizards to be a pretty good basketball team. John Wall was exceptional after returning from injury last year, and the Wizards responded by playing .500 ball. Gortat will help, and Beal could be a star in the making.
I'm pretty in-between on Indiana and New York. A few analysts think they'll be 50+ win teams, a few think they'll be around the 40 range. My model says both teams will be in the mid-high 40's, which seems reasonable.
Projecting Cleveland and Minnesota was a mess.
Denver surprisingly looks like they could make the Playoffs. I thought the Iggy-escape would cripple their rating, but I guess not.
The Lakers and Celtics will be terrible this year. Doc Rivers better not screw up the Clippers.
The West has 6 title contenders, the East has 3. BUT, the East has (arguably) the best 2.
That's all for now. The NBA is upon us, so rejoice, prep your couch and television set, and get any homework done ahead of time (for procrastinators like me).
Welcome to the 2nd installment of the Head to Head series, where we compare two marquee players in the games where they faced off, and determine who won the epic duel. Today's matchup features Derrick Rose and Russell Westbrook, two players with so much explosive athleticism, that the court is prone to spontaneous combustion when they both take the floor.
As explained in the prior Head to Head article, this evaluation utilizes the Alternate Win Score (AWS) metric, which has been found to be the best linear box-score metric available, to rate individual games. Readers should also be familiar with this page, which includes the box-scores of every Westbrook/Rose matchup.
This week as opposed to simply adjusting for playing time, I adjusted for pace and opponent strength as well. The first column on the table below (AWS48) simply compares each player's raw AWS score when adjusting for playing time. The next column (AWS P) goes a step-further by adjusting for pace. The final column (AWS O) concludes by including an adjustment for strength of opponent.
Both Rose and Westbrook have played each other to a relative stand-still in their short 6-game head to head career. While Rose has a slight advantage before the opponent strength adjustment, its Westbrook who ends up with the slight lead once all factors are accounted for. This makes sense, as Rose tends to play on better defensive teams, which would give him a slight boost in terms of raw output. Now for a more colorful table utilizing AWS (O).
- It should be first noted that Rose and Westbrook are fairly mediocre when playing against each other. I'm not sure why.
- Their Mutual Triumph game wasn't very spectacular, and occurred in their sophomore season.
-The Mutual Fail also wasn't too extreme, especially considering that it occurred in their rookie years.
- During the 2011 season (Rose's MVP year) Westbrook outplayed Rose in both matchup games. This was highlighted by Westbrook's "Own" game pictured above, where he put the smack-down on Rose.
-Rose had his turn demolishing Westbrook, but it occurred in the obscure 2010 season, before either player was considered a superstar.
Overall, I can't decide on a clear winner. Westbrook seems to have a slight edge, but its not really significant, especially since we have an extremely small sample size of 6 games. This matchup will be something to watch for this year, assuming neither player is hampered by lingering injuries.
AP Photo/Kevork Djansezian
Welcome to the first in a new series called, "Head to Head", where I compare two premier NBA talents, in the games where they, well, went Head to Head. We're kicking things off with two of the greatest Shooting Guards of all time, Kobe Bryant and Dwyane Wade.
Warning, this piece will utilize Alternate Win Score, a linear box-score metric devised by Dan Rosenbaum. Its essentially the best linear metric to use when evaluating individual games. Readers should also be familiar with this page, showing the game-logs of every Kobe/Wade Head to Head matchup. The following table is per-48 minutes.
It looks like Dwyane Wade has outplayed Kobe Bryant during the course of their head to head careers. Kobe has been the better scorer, but Wade has been better at almost everything else. Its close though, as Wade trumps Kobe by less then a point.
Now for a more interesting (and colorful) table.
- The "Mutual Triumph" refers to the game where both Kobe and Wade were playing well, which refers to the second LAL/MIA matchup of this past season.
- The "Own" tabs refer to games where each player was basically destroyed by the other. Wade's victory comes in a Christmas game of the 06-07 season, while Kobe's comes in a 2003 game, where Wade was a rookie.
- The "Mutual Fail" tab alludes to a game where both players were struggling, which for this duo, occurred in the 10-11 season. It was the first meeting between the Big-3 of Miami, and Kobe's Lakers.
All in all, its pretty clear Wade comes out on top. His overall rating is higher, and he won all of the "sub" categories in the table above. Its also a blow to Kobe, that a significant portion of his rating came from beating up on a rookie Dwyane Wade in 2003. That's just mean.
Just a quick update for the Position Adjusted XRAPM stuff I've been doing. I decided to switch from my earlier 3-Position system (Bigs, Wings, Guards) to the traditional 5-Position system.
This was my original goal, but for some reason I couldn't get any respectable correlations with winning percentage or original XRAPM, when utilizing the 5-Position adjustment. That is, until quite recently, which is why I'm making the changeover.
Original XRAPM - Position Adjusted XRAPM Correlation
Original XRAPM - Winning % and Efficiency Correlation
Adjusted XRAPM - Winning % and Efficiency Correlation
There was a sizeable drop between the 3-Position/Original XRAPM Correlation (93%), and the new 5-Position/Original XRAPM Correlation (84%). As depicted by the first table, this is almost entirely due to the decreased D-XRAPM Correlations to each other. I can't complain, as that was the original goal.
What's impressive however, is how well the new 5-man Position Adjusted XRAPM Correlates to Winning Percentage, Offensive, and Defensive Efficiencies. There isn't much drop-off, either compared to the Original or 3-Position XRAPM. In fact, the new 5-Position XRAPM has a stronger correlation to defense (89%), then the earlier 3-Position XRAPM (84%).
I'll be posting the new 5-man Position Adjusted XRAPM numbers for the top 30 under the same tab at the left.
Recently, I've noticed a lot of "how to beat the Heat" articles around the web. Most are complete nonsense, but others have some merit. I decided to take a cursory look at the issue, using an approach rooted in statistics.
The first step was to calculate the major rate stats for the Heat and their opponents, for every game they played in the 2013 season. I then regressed these values on the Heat's Offensive, Defensive, and Net Efficiency (Point Differential) for each respective game, to see what factors correlated to a Heat win, or loss.
I thought about using games dating all the way back to 2011, but the Heat have changed so much over the years, that the only relevant dataset seemed to be this past season.
The values in the table below indicate the strength of the correlation (0-100) between two factors. A large positive number indicates a strong correlation that's good for the Heat (high TS%=high Offensive Efficiency), while a large negative value indicates the opposite. Numbers near zero indicate no significant correlation.
A few takeaways from the above table.
The two major things a team can do to beat the Heat is shoot efficiently, and stop the Heat from doing the same. Though this is a no-brainer, its also extremely difficult.
The next best idea is another no-brainer. Don't turn the ball over. Although, judging by the stronger correlation to Steals then Opponent Turnovers, it would seem Miami would prefer to force live-ball turnovers.
Something to note, is the relative lack of influence rebounding has on the Heat's ability to win games. This is significant, as Miami's rebounding woes are frequently discussed, especially after games where the Heat may be outrebounded by as many as 30.
Another interesting tidbit. Fans of opposing teams usually detest Miami's ability to land free-throws, while seemingly preventing their teams from doing the same. However, both Miami and their opponent's Free-Throw Rates don't seem to significantly impact the game's outcome.
Its often said that great passing teams can exploit Miami's overaggressive defense. However, there doesn't seem to be a significant correlation between an opposing team having a high Assist-Percentage, and outscoring the Heat.
That's all for now. Hopefully it was informative. One should take care not to see this as the end all be all Miami cheat-sheet. There are many more layers that could be added to this study, before we would approach statistical certainty.
As many of you know, I did a thing where I averaged out 6 of the major NBA stats to form a "consensus player rating", which was/is at a tab on the left. I decided to redo it, but to try and weight each metric in the blend pool, as opposed to simply averaging them together.
The relative weight of each metric was determined by averaging its correlation to past wins with its correlation to future wins, thereby rewarding accuracy. Correlations for future wins was pulled from Neil Paine at APBR, and I did the past wins correlation for 2012-2013 myself.
I dropped EZPM from the pool, leaving us with Wins Produced, Win Shares, PER, ASPM, and XRAPM. I standardized each metric, summed and regressed them on 2012-2013 wins, weighted them, and added them together.
Surprisingly, there wasn't a huge difference in weights. PER had the lowest, at around 17% of the blend, and XRAPM had the highest, at about 22% of the Blend.
I'm also regressing each metric and its components (O-XRAPM, D-ASPM, etc.) on "basic" stats like TS%, ORB%, and OFF EFF, to see what makes them "tick". That will be released later in the week, but I just wanted to let everyone know what's up.
The new "Blend Score" Ratings for the Top 40 players will be posted on a tab at the left, in place of the Consensus Ratings of the past. The table includes the standardized Blend Score, as well as each players WP48, WS/48, ASPM, PER, and XRAPM, also standardized, so you can compare across metrics.