Tag Archives: statistics

Olympic Basketball Beijing 2008 Raw Statistics

As with Olympic Basketball Statistics in London there’s a strange lack of easily available data for tournament-wide analysis. Official Page for US Basketball has a pdf file for each team but they are not import-friendly. Official FIBA Page is again divided into leaders in many categories which make it difficult to see players’ entire stat-line… you can find it on individual pages for the players but good luck doing it 144 times. Even usually reliable dropped a ball here so I guess it’s up to me now ;-)

I collected boxscores from all games from ESPN and simply added them together for the following results… Table is sorted by a team and then by total minutes played.
You can also check those statistics per 36 minutes or compare to Games in London in 2012
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Posted by on September 21, 2012 in Fringe Stats


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London 2012 More Basketball Statistics for Men

While it’s too difficult to find even raw statistics from Olympic Basketball Games 2012 in London it’s just flat-out ridiculous if you are interested in something more than that. So… All numbers below are per 36 minutes.

WS stands for Win Score which is calculated PTS + STL + ORB + 0.5*DRB + 0.5*AST + 0.5*BLK – TOV – FGA –0.5*FTA – 0.5*PF while GS stands for Game Score where the formula is PTS + 0.4 * FG – 0.7 * FGA – 0.4*(FTA – FT) + 0.7 * ORB + 0.3 * DRB + STL + 0.7 * AST + 0.7 * BLK – 0.4 * PF – TOV.
Table is sorted by a team and then by average minutes played.
You can compare those numbers to statistics from Beijing in 2008.

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Posted by on September 19, 2012 in Fringe Stats


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Playground Tournament between Advanced Metrics?

Because I play pick up games and fantasy sports I often wonder…how would league/tournament look like if NBA players were distributed among teams by different metrics?

You can approach this topic in many ways but metrics agree about value of top stars so I don’t think standard draft of all players could work without issues typical to snake drafts in fantasy NBA.
Instead I wanted to focus and magnify differences in evaluation and here’s a twisted idea:
based only on last season’s numbers I subtracted player’s ranking according to given metric from average ranking for him based on 4 other statistics and sorted it by the biggest difference for each metric. In other words, I wanted to find players for each statistic which were valued the most by it when compared to others.

To make it clear, let me give you an example, last season Andrea Bargnani was ranked by PER as 80th best player in the league. Win Shares, Wins Produces, RAPM and ASPM graded him below Top200 [to be exact 216th, 245th, 231st, 209th for an average of 225.25].
So PER viewed Bargnani’s 2010-11 season 145.25 slots higher than the competition, and it was the highest difference for it, so he would be selected for the tournament as a representant for PER ;-) Got it?

The rest of such draft would look like this… [I included 9 top players for each metric with difference in parenthesis and extra players were mentioned because of positional need]
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Posted by on January 28, 2012 in Unanswered Questions


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Advanced statistics agreed about those NBA players

Yesterday I explored topic of the biggest differences between perceived value for some NBA players among five advanced statistics and I don’t want to be only negative ;-)
So for a change let’s spotlight those examples where metrics mostly agree, shall we?

Again based on great database prepared by Alex at and for the last 5 years I ranked each player [with at least 1000 minutes played] according to five different metrics [PER, Win Shares per 48 minutes, Wins Produced per 48, new RAPM and ASPM] and then I calculated standard deviation of each player’s ranking and below you will see only those with the lowest number each year meaning the differences in evaluation between statistics were the smallest.

Again let’s start with the most recent season…
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Posted by on January 26, 2012 in Expanding Horizons


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Effects of No Rest between Games on NBA Players

There may not be NBA season but with a possibility of more packed schedule than usual due to the lockout
I wondered what are the effects of no rest between games and how important are those days providing a break. There are studies available about this issue on a team level but I haven’t seen one on a individual level.

So I’ve collected Yahoo’s split statistics for 953 player-seasons [those who played at least 41 total games in the last three years] and divided them into 5 groups based on a different situation:
“Total for a season”, “0 Days Rest”, “1 Day Rest”, “2 Days Rest”, and “3+ Days Rest”.

Here are basic averages of those groups:
[where WS/36 = Win Score per 36 minutes and GS/36 = Game Score per 36. Also please note that shooting percentages where calculated on a league level so FG% = “sum of all avg fgm” / “sum of all avg fga”]:

Situation GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
Season 68,7 24,73 46,04 36,19 76,79 4,22 2,20 0,75 1,39 0,49 2,11 10,33 6,32 10,81
0 days
of rest
15,7 25,18 45,86 36,58 76,91 4,24 2,17 0,73 1,40 0,49 2,17 10,43 6,11 10,58
1 day
of rest
34,0 25,12 46,02 35,94 76,88 4,30 2,25 0,78 1,41 0,50 2,13 10,47 6,37 10,85
2 days
of rest
10,8 24,67 46,37 36,54 76,81 4,21 2,23 0,75 1,39 0,49 2,10 10,38 6,34 10,85
3 days
of rest
8,2 23,01 46,08 36,26 75,68 3,99 2,08 0,73 1,32 0,49 2,02 9,73 6,31 10,82

4-5% loss maybe isn’t much of a difference but back-to-backs are indeed the worst and 1-to-2 days of rest seems the best time to play. On average that’s because of fouls, steals, rebounds, blocks, assists and FG%.

It pretty much confirms basic principles from a team level to an individual level so I’d like to dig in a little deeper.

For example, which group of players is affected the most by lack of rest? Those old veterans, right?
(Data about player’s age during each season was taken from,
“#” means “number of players in this range”)
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Posted by on November 14, 2011 in Scrutiny


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