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Posts Tagged ‘Stats Tuesday

Stats Tuesday: Some random thoughts on the Denver Nuggets in 2012-2013

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Denver Nuggets logo

Denver Nuggets logo (Photo credit: Wikipedia)

The Denver Nuggets have emerged as a popular sleeper pick among the statistical community. John Hollinger picked them to finish 2nd in the West (ahead of the Thunder and Lakers), Basketball Prospectus picked them to finish 1st in the West, and the Wins Produced/Wages of Wins picked them to finish 2nd. The Nuggets last year finished 6th in the West last year with a 38-28 season, equivalent of 47 Ws over 82 games. Where does the extra optimism come from?

The line of reasoning for such Nuggets break-out essentially breaks down to:

  1. The Nuggets were dominant offensively last year (3rd ORTG) despite injuries to Danillo Gallinari and Nene slightly derailing them early in the season, as well as one of their most productive offensive players in Kenneth Faried not getting minutes early.
  2. They were however disappointing defensively (19th DRTG). However, they added one of the very best defensive players in the league in Andre Iguodala, as well as another great athlete in Wilson Chandler.
  3. With a shored up defense and elite offense, this is a combination worthy one of the league’s best.

I have a few objections to this Nuggets’ improvement. One is I could see them taking a step back offensively. Read the rest of this entry »

Written by jr.

October 23, 2012 at 9:36 pm

Stats Tuesday: Should “replacement efficiency” be used instead of league average efficiency, in NBA comparisons?

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Derrick Rose at a promotional appearance.

The value of Derrick Rose’s efficiency in his MVP season is questioned by the advanced stats community (Photo credit: Wikipedia)

A hot topic among basketball nerds is what to do with players who shoot either a league average efficiency or a below average one. Our instincts tell us a player who shoots an average shooting efficiency when he has teammates who’s efficiency is well above average, is a problem. Because it indicates the player could be passing the ball more to these more efficient players, thus raising his team’s efficiency. It indicates that if the team’s efficiency is above league average, that the credit for this should be relegated to the players taking above average shots in efficiency, not the one taking a ton of possession at an average efficiency that doesn’t move the meter.

To use an example, in the last non-lockout year (2010-2011) league average True Shooting Percentage/TS% (incorporating 3s and FTs, essentially creating a points per shot metric) was .542. The MVP, Derrick Rose, had a TS% of .550. Kobe Bryant’s score was .548, Carmelo Anthony’s .557. They are considered superstar scorers in this season because of their volume points per game. But using a strict model of comparing volume and efficiency can create some shocking results. Take the two examples of Tyson Chandler and Nene, both not known for scoring talent, but among the league leaders in efficiency in 2010-2011. Chandler takes 7.26 shots a game in the regular season on the Mavericks (using the calculation FGA + 0.44*FTA) at .697 TS%. Multiplying Chandler’s volume of shots (7.26) times league average efficiency for points per shot (.542 TS%) adds up to 3.94 points. At Chandler’s real efficiency (.697) he scores 5.06 points, for a margin of approximately +1.12 points from average. Nene likewise has 11.1 shots at .657 TS%, using the same calculation as with Chandler he ends up adding +1.27 points compared to what his shots taken at average efficiency would create. However look at what happens when the same calculation is done with Rose, Melo and Bryant. Rose, taking 22.74 shots would create 12.3 points if had shot at league average efficiency, while at his real efficiency of .55, creates 12.5 points, a whopping difference of +0.2 in the points column. Carmelo, using 22.98 shots a game at .557 TS%, using the same calculation ends up adding about +0.35 pts compared to if those shots had been taken at an league average level, while Bryant at 23.1 shots converted at .548 TS%, ends up adding a measly +.14 points compared to the average conversion of those shots. All 3 of Rose, Melo and Bryant’s scores not only trail Chandler and Nene’s numbers, but they’re not even in the same ballpark.

This is why statistical attempts to quantify scoring have met such difficulty. Read the rest of this entry »

Written by jr.

October 9, 2012 at 10:32 am

Stats Tuesday: A statistical case for Miguel Cabrera as American League MVP + Should a runs involved stat replace RBIs?

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English: Miguel Cabrera at Dodger Stadium.

English: Miguel Cabrera at Dodger Stadium. (Photo credit: Wikipedia)

The AL MVP race is the most interesting in years due to the debate of traditional stats vs new age sabermetrics, as many have pointed out. As of October 1st, 2012, Miguel Cabrera is in position to win the Triple Crown of leading the league in the traditional hitting stats Batting Average (BA), Home Runs (HR) and Runs Batted In (RBIs), but Mike Trout holds a significant lead in Wins Above Replacement (WAR), 10.7 to Cabrera’s 6.8 according to baseball-reference.com, a massive gap.

Trout’s gap in WAR comes from two places. One, he is given 2.3 points in defensive WAR due to excellent fielding in center-field, while Cabrera’s score is -0.2 at 3rd base. Secondly, Trout’s offensive WAR (8.6) beats out Cabrera’s score of 7.4 on baseball-reference. This is interesting because it is clear that Cabrera is the better hitter statistically, leading in OPS, batting average, slightly trailing in on-base percentage, and having a sizeable gap in home runs. A fascinating statistic is that Cabrera has 137 Runs Batted In (RBI) to Trout’s 83.

RBIs have of course lost favor in recent years for obvious reasons. They’re simply too context based. A player’s RBIs depends on the ones in front of him who get on base. In this case, Trout’s offensive value is more reliant on scoring from the bases than Cabrera’s is. For one, he hits lead-off, a position where where worse batters behind him mean there’s less chance of players to be on base – and the first bat of the game no players can be on base. Secondly, Trout is a fantastic base-runner and base stealer (Leading the AL in stolen bases at 48, amazingly only being caught 4 times). Third, by the fact that less of hits are home runs than Cabrera’s despite a higher on-base percentage, this is another reason why more of his hits end up with him on the bases, waiting for the batters in front of him to hit him in. RBIs of course can’t measure the value Trout brings by getting into scoring position. When a batter in front of him hits Trout in, they are credited with an RBI, while he is given a Run Scored, which is tracked but not given much weight in awards voting. Trout, unsurprisingly, leads the AL in Runs Scored by far with 129 – Cabrera coming in 2nd at 109. Trout leading Cabrera in offensive WAR comes down to favoring this “scoring from the bases” advantage Trout has over Cabrera, outweighing the extra damage Cabrera does at the plate.

We know Trout’s Run Scored nor Cabrera’s RBIs advantage over each other isn’t indicative of their value. Their roles are different, Trout’s favoring scoring off the bases and Cabrera with the bat. This is concerning because fundamentally, we should want to measure how much runs a player actually scored in a game, in a same way we want to know how many points a basketball player actually scored.

So what I came up with a little stat to try and incorporate both runs batted in and runs scored off the bases for a player. I can’t be the only one who’s tried this, but nonetheless here’s what I did: I took a player’s Runs Scored and subtracted Home Runs, as when a player scores a Run off a homer, it’s counted as an RBI. A good term for the total Runs Scored not counting Home Runs, is “Baserunning Runs Scored”. I then added this number to RBIs. The stat thus is simply (Runs Scored – HRS) + RBIs, or Baserunning Runs Scored + RBIs.

Thus this counts the “Total Runs Involved In” for a player, accounting for the runs a player scores either with his bats or from baserunning Read the rest of this entry »

Written by jr.

October 2, 2012 at 1:35 pm

Stats Tuesday – The future of Demar Derozan and the possessions game catching up to young players

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DeMar DeRozan of the Toronto Raptors {| class=...As a Raptors fan, I’ve been asking myself “What to do with a problem like Demar Derozan” Sound of Music style

By the old way of judging players, Derozan scoring 17.2 points and 16.7 points a game in the 2nd and 3rd seasons would seem evidence he’s a player to build around in a starting lineup. It’s not easy to get 16 point a game+ scorers.

Advanced stats say otherwise. A .530 TS% and .503 TS% the last two years and little other impact on the game but scoring, bring him to a PER of 14.4 and 12.8, the former below average and the latter awful.

The boogieman for Derozan’s career going forward is possessions. Using the equation of FGA+0.44*FTA+TOV to measure scoring possessions, he averaged 18.0 and 18.6 possessions a game. This is a lot. To use a comparison, last year Paul Pierce used 19.9, Joe Johnson used 18.8 last year, Danny Granger used 19.1. So Derozan’s 18.6 possessions a game last year is fairly close to star wings’.

The problem for Derozan is the only reason he’s getting these possessions at his current caliber of play is the poor quality of his team’s offensive options. He does not have the talent to be a top scoring option on an elite team, based on what he’s shown so far. The way we know this is the players who do have “top scoring option on a great team” talent, if given the keys on a bad team, will typically produce at a much higher volume than Derozan did last year. Granger in 2008-2009 averaged 25.8ppg, then 24.1ppg in 2009-2010. Joe Johnson averaged 25.0ppg in 2006-2007. By comparison’s Derozan’s 17.2 and 16.7ppg seasons are fairly meek, especially at a poor efficiency.

In basically any situation, a score first player is going to get less possessions on a talented team than on a poor one. This seems inherently obvious – The star offensive player has to carry the team with less help. Read the rest of this entry »

Written by jr.

September 25, 2012 at 1:16 pm