Methods of contacting me:

  • Comments on this site
  • Twitter (DSMok1)
  • Email: my twitter username at


  1. Sean

    I didn’t see a post regarding the 2011 US Open. Considering your work proved to be spot on for the Masters I thought for sure you would have done another “prediction”. I know the NBA Finals take precedence and you put forth a lot of time towards that endeavor. Hope you consider the British Open and PGA if you have the time. Thanks for your efforts! Great work and I love the side. Thank you.


    • DanielM

      Yes, I’ve been really busy lately, with real world stuff. Moving across the country soon. I will likely have less time to work on this site as I transition to a new job. I have done some more work with golf, but haven’t had a chance to follow through with it. I have improved the system–Charl Schwartzel really shouldn’t have been favored; that was an artifact of a lack of connectivity within the regression! (That worked out pretty nicely, though.)

    • DanielM

      Contribution to the team’s efficiency bottom line. It is equal to the player’s ASPM*%min. So if a player is a -1 player (below average, above replacement level) and plays 50% of the minutes, the contrib for that player would be -0.5.

  2. Marko

    Is there any way to use ASPM to create NBA betting lines that are comparable to Vegas lines to judge overvalued and undervalued teams? I know you’ve done a few game predictions but I haven’t seen anything consistent and that considers ATS.


  3. Wayne Hagarty

    Daniel, might there be some daily NBA daily predictions/predicted scores once the regular season commences? They are always a great talking point amongst NBA purists & they keep us coming back to your site, day after day.

    Appreciate your time.

    • DanielM

      Thanks for the contact, Wayne.

      I don’t plan on doing any daily predictions, no. I don’t put much time into stats right now, certainly not on a daily basis. I think TeamRankings might have some NBA stuff like you mention?

  4. Zhou Yu

    Hi Dan,

    You are absolutely amazing to share all the data you collected from Google docs (rather than selling it). I sincerely appreciate it.

    Question – is there any where I could find a reference for the explanation of each metric? Some of them are obvious but many of them may need some explanation.

    Thank you,

    • Zhou Yu

      Hi Dan,

      Also not sure if your data is from different sources. Some minor error I found, for example, Kobe Bryant was hurt in 2013 but not shown on your data (no hurt marked in that column).

      Do you have a sense which column in your data is less reliable than others?

      But still, thank you so much for sharing it.


  5. Eric

    Mr. Myers, I have read your BPM article on basketball-reference with great interest. I am in the midst of a project comparing four methods (yours, WS, WP, and EWA), and I can’t figure out one part of your methodology: replacement level is -2.0 BPM, but what is the win total for a team of replacement level players? If I sum up all the VORP for 2014, I get 810.2 wins above replacement level, which leaves 420.2 for the replacement level, which divides rather neatly to 14 wins. Is that a coincidence, or is 14 wins the number? I get 810.8 wins for 2013 too but I thought asking directly was most prudent.

    • Daniel M

      Since a team of -2.0 players will produce about 14 wins (depending on how you do the team points to wins conversion–pythagorean method, linear, etc.), that is the number of wins a replacement level team will produce according to VORP.

      The number that was derived directly was the -2.0 value, per the extremely long thread on Tom Tango’s blog (linked to in the BPM writeup). A team of replacement players will produce some wins; the difficulty lies in establishing that number. Several methods were used in the later portions of the Tom Tango comment thread, and all basically came up with the -2.0 value.

  6. Kevin Martell


    I’m loving your Box Plus/Minus stat. One thing I noticed is that guards seem to have better BPM and VORP than bigs. Is this an accurate observation? Is there anything in BPM that favors perimeter players over bigs? Thanks!

    • Daniel M

      Over the entire time period that BPM covers (since the mid 1970s), there is no significant positional bias; all positions average near 0.

      I just checked a bunch of time intervals, and there is no strong bias anywhere. Maybe a slight tilt toward small forwards, (averaging about +0.25), but there are a number of possible reasons for that.

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