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Tag Archives: Russell Westbrook
Game of the Week: Memphis Grizzlies at Oklahoma City Thunder
This is a big one. This game matches Oklahoma City’s league-best offense against Memphis’ league-best defense. It’s as big as a January game gets. Let’s get into it. Key Players I’ve already poured out my love for Marc Gasol in our All-Star roundtable, so I’ll focus on the other Grizzlies big man, Zach Randolph. If you watch Z-Bo under the boards, you understand why he’s such a strong rebounder. He’s as physical as anyone … Continue reading
Weekly Stats Recap
As mentioned in the previous edition of the Weekly Stats Recap, the suggested #StatStudy for this week was orchestrated to determine the impact of elite assist men. Perry Missner (@PerryMissner), a noted doubter of the importance of great point guards, estimated that 65% of the teams with a double digit dime man would win. As it turns out (for this week at least), Perry wasn’t pessimistic enough when it comes to the correlation between … Continue reading
Weekly Stats Recap
This week’s #StatStudy was suggested by our very own Ian Levy (@HickoryHigh), a great basketball mind who will stop at nothing to make sense of this game we all know and love. Mr. Levy was curious as to the ideal number of possessions, a stat that I measured by recording the OffEff (points per 100 possessions) of every winning team this week (52 games). Oddly enough, the result from this study closely resembled a … Continue reading
Evaluating Team Shot Selection
A few weeks ago I shared a new metric I had been working on called Expected Points Per Shot (XPPS). The idea was to create a metric that would allow the objective comparison and evaluation of the quality of a player or team’s shot selection. When I first shared XPPS we just looked at the numbers for individual players. The visualization with player data is now a permanent feature here, one that I’ll keep updated … Continue reading
Weekly Stats Recap
For this week’s study my brother (@KurtSoppe) pondered if FG% or FT% was more directly correlated to the game’s final outcome. He hypothesized that both FG% and FT% would be consistently higher for victorious teams, with the stronger correlation being found in the field goal percentage. In support of Kurt’s guess, winning teams shot an average of 47.9% from the field while losing teams shot only 42.3%. Winning teams shot better than 50% from … Continue reading
Weekly Stats Recap
This week I had a stat request from Jesse Silverman (@JesseBeau), a Cortland intramural champion and defending March Madness king. He wondered if the number of double digit scorers was directly correlated to victories, guessing that winning teams would have more double digit scorers in each game. As it turns out, there was not a considerable difference in the number of players in double figures for winning and losing teams. Over this holiday week … Continue reading
Weekly Stats Recap
This week’s study was suggested by college basketball guru and FSWA (Fantasy Sports Writers Association) gate keeper Perry Missner (@PerryMissner). He is a passionate basketball fan and his suggestion led to some very interesting results. Perry wondered which was more directly correlated to success: quantity or quality when it comes to field goal attempts. I ventured a guess that FG% would be a better predictor, but figured it would be a close study. After … Continue reading
Weekly Stat Recap
This week’s study was done to determine what position led the winning team in scoring with the most frequency. For all 53 games this week I recorded the position of the highest scoring player on the winning team and the percentage of his teams points he scored. I used ESPN’s box scores to determine what position each player qualified as. In a resounding 35.8% of the games this week, the winning team was led … Continue reading
Weekly Stats Recap
This week I took a look at which statistic had a greater impact on a game’s outcome, transition points or points in the paint. I charted the fast break points and interior points for each of the 49 games this week and broke them into two separate groups: winning and losing teams. I then divided the totals of the losing teams by the number of points scored to determine what percentage of points were … Continue reading
