The 2015-2016 season is underway and like last year you can find your advanced DBL stats in our Downloads section. You can still use Excel to do your own analysis, as described in Using Excel for analysis.

Feel free to contact me (see About for contact details) if you have any questions or suggestions.

I have added a csv file to the output directory that calculates totals per team for the basic box score data, offensive ratings, defensive ratings, net ratings and four factors for the team an their opponents.

See the “*team_totals.csv” files in our Downloads section.

The 2014-2015 season is underway and like last year you can find your advanced DBL stats in our Downloads section. You can still use Excel to do your own analysis, as described in Using Excel for analysis.

Feel free to contact me (see About for contact details) if you have any questions or suggestions.

To evaluate a team’s scoring efficiency by its players, I like to compare:

• how often a player is used in absolute terms offensively (median of the player’s finishes)
• how often a player is used in relative terms (median of the player’s usage)
• how efficient a player is scoring-wise (player’s points per play)
• player’s playing time

I find the following chart useful to graph this:

Read more...

The 2013-2014 season is underway and like last year you can find your advanced DBL stats in our Downloads section. You can still use Excel to do your own analysis, as described in Using Excel for analysis. This year, we provide some pre-configured Excel files; see Downloads.

Be sure to check out Math professor Jeff Bergen explaining the odds of picking a perfect bracket at FlowingData blog.

Apparently, Kevin Pelton, Bradford Doolittle, and John Gasaway have left basketball Prospectus and are now writing for ESPN insider.

That’s a shame … I liked them in my geeky, sporty corner of the internet that basketball prospectus had been for me. But I guess I’ll have to change the sweet, late-nineties styled Basketball Prospectus with it’s iconic yellow banner (“Click Here to Subscribe Today!”) for the horrible, flashy ESPN Insider with “Exclusive Rumors”.

With the DBL season just underway at about six games per team, what are the statistics that can already reliable tell us something? In his article “Calling the Race” [1], Kevin Pelton of basketballprospectus.com takes a look at the NBA season 2010-11 and examines from what point in the season statistics align with the eventual outcome at the end of the season. What can we learn from Kevin’s article?

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When estimating the number of possession, we have to estimate:

• The number of times a team went to the free throw line as a result of a foul, that wasn’t preceded by a shot. This is what I call a free throw trip.
• The number of times a possession continued because of an offensive rebound.

I have chosen to use an fta factor of 0.46, based on references of Oliver [1] and in-the-game.org [2].

Here are the formulas I use:

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Using Excel for analysis

If you’d like to do some analysis using Microsoft Excel, you can start using the analysis_2012-2013.xlsx Excel document located in the downloads section. This assumes you’re using Excel 2010.

The document consists of three worksheets:

• Dashboard with three pivot tables, as an example.
• TeamStatsFromWeb, the advanced team stats, used as the data source for two team pivot tables.
• PlayerStatsFromWeb, advanced player stats. used as the data source for the scoring pviot table.

The workbook has two data connections that point to the online advanced stats csv files for the 2012-2013 DBL regular season. To load the data from the web, you should be able to do:

Data > Connections > Refresh All

The data in your dashboard might not immediately update; you might have to manually refresh your pivot grids too:

PivotTable Tools > Data > Refresh

Note that we can’t calculate a median using a pivot table in Excel, therfore in the examples on the dashboard worksheet I used a simple average function. This will calculate per-game averages, based on the per game values in the advanced stats.

This site is used to publish data and findings of my analysis of the Dutch Basketballeague. The analysises are performed using R, and the source code is available on Github. I’m no statistician and would welcome any constructive feedback, feel free to fork me on Github and send in your pull requests.