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The 2010-11 Fantasy Basketball Season in Two Dimensions

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Categorized as: Author: Patrick, Fantasy Basketball, Fantasy Basketball Strategy
Posted on: June 1st, 2011

Have you ever wondered what a visual representation of NBA players would look like using a two-dimensional scatter plot? Probably not (unless you’re a strange dude like me), but that’s exactly the question I asked myself the other day.

Using a statistical technique called multidimensional scaling (MDS), I collapsed the nine traditional statistical categories used in fantasy leagues (PTS, REB, AST, STL, BLK, 3PT, TO, FG%, FT%) for each player into two dimensions, which could then be plotted on a chart. MDS does the heavily lifting statistically, but it doesn’t have a clue conceptually what the dimensions are. I took a shot at naming them based on what I saw: a move from big men to small men going from left to right and the way all-stars rose to the top of the chart while role players fell to the bottom.

How does MDS work? The further two players are away from each other on the chart, the more dissimilar they were this season. According to the results below, the two most dissimilar players in the league this season are probably Dwight Howard and Kevin Martin. Conceptually that makes sense. In addition to having completely different games, Howard is the league’s worst free throw shooter, Martin’s is the league’s best.

Because the chart is small, I broke it out by position to help with readability. You can adjust the position using the slider below the chart. You can also view the chart by team. There is a ‘show history’ button that will show the cumulative results as you move along positions. It can be unclicked if the chart gets too cluttered. Finally, you can mouseover any dot on the chart to get the player associated with that item.

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Just looking at the centers, you can see that Dwight Howard deserves to be in his own separate chart, preferably on another planet away from everyone else. Other than Howard, Andrew Bogut is the most “centerish” center, while Andrea Bargnani comes off as the most guard-like. I think that means this chart passes the smell test.

A note that stats are from the 2010-11 season and are based on per game averages. Players who didn’t finish in the top 200 for the season overall were also removed, as were guys like Yao Ming who missed more than half their team’s games.

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  • 714frit

    Why do you love Shaq?  Here’s why I do…

    http://ssssssssssss-shaq.blogspot.com/

  • http://basketball.razzball.com/ Adam

    I love this shizz. Guys, I enjoy your in-season stuff, but your pre-/post-season tidbits are just great. Hats off to you.

    Keep it going.

  • Terrance

    this.is.great! Love the graphs!

  • Terrance

    not surprisingly, boris diaw is the league most average player.

  • Phoenix

    you know what would be cool? if you charted the history (2010-2011, 2009-2010, 2008-2009) of each those players and connected those dots. then did a statistical aggregation/analysis over time, based on how fast they rose to where they are now.  Perhaps this type of chart could help us predict the future and who’s most likely to boom and bust?

  • http://givemetherock.com/ Patrick

    I could not think of a more fitting title for Boris Diaw…

  • http://givemetherock.com/ Patrick

    That sounds awesome. And a lot of work, but awesome.

    Actually one of my pet projects at the moment is to try to find a way to predict future fantasy performance, so I like what you’re saying. If I ever get a chance to look at player value over time using that type of analysis, I’ll post about what I find. Thanks!

  • Phoenix

    yeah, there’d be so many variables that you’d have to put into, including likelihood of injury, type of coach, change of coach, type of playing system they’ll be in, style of coach, style of college coach, degree of professionalism, personal demeanor (driven and coachable versus i don’t care what other people say, i’m gonna play my damn way (Durant versus AI)), etc.

    That kind of stuff, I bet people would pay for, good sir.  Perhaps a partnership with Basketball Monster would be right up your alley, considering they already have all the stats and do predict current NBA players, which you probably already know. 

    Would love to discuss further and look forward to your findings (provided that you have the time, of course).