This is a very different look at the data than I would normally do when approaching a full season. This approach is a game by game approach, attempting to quantify each and every players’ statistical impact in each and every Tourney game. I calculate my version of statistical +- for every player in every game, and using those results dole out every player’s share of the win or loss for each game. Here’s the results, compiled overall and per game:
It’s late, I don’t have time or the gumption to go into a ton of details here – all the data is in the spreadsheet and fully sortable. Copying from the FAQ page, here’s a synopsis of the main ratings I am utilizing here and how they generally work:
|PW||Player Wins. All players with >0 Statistical +- in a win will get a share of the win proportionate to their Stat+- relative to the others. All players <0 Stat+- get a 0 PW for that game.|
|A player might get a full 1.00 PW for a game, if he played a great game in a win & was the only player on his team with a >0 Stat+-|
|Summed PW for every player on a victorious team will equal 1. Compiled PW for this tourney equals 67 (67 victorious teams).|
|PL||Player Losses. All players with <0 Statistical +- in a loss will get a share of the loss proportionate to their Stat+- relative to the others. All players >0 Stat+- get a 0 PL for that game.|
|A player might get a full 1.00 PL for a game, if he played a HORRIBLE game in a loss & was the only player on his team with a <0 Stat+-|
|Summed PL for every player on a losing team will equal 1. Compiled PL for this tourney equals 67 (67 teams lost a game).|
|Stat+-||Statistical +- is a theoretical +- player stat based on his box score data relative to that specific game. A great game statistically = good positive Stat+-, a BAD game = negative Stat+-|
|A player might have a BIG Stat+- in a blowout win, but have a relatively small PW because many other teammates had good Stat+- & took a good share of the PW.|
|Compiled player Stat+- for any team in a game will exactly match the final game outcome margin. If a team wins by 5, the compiled Stat+- of the players will equal 5.0|
There were 7 games that stood out the most in this tourney – in order of Player Wins:
|1.00||0.00||1||15.1||13||Derrick Walton Jr||Michigan||1||39||26||5||11||2||1||3||3||78.8%|
|0.76||0.00||6||24.8||1||Kennedy Meeks||North Carolina||5||30||25||14||1||3||1||0||4||78.1%|
The spreadsheet has all 1295 player games (including all the raw data if anyone wanted to even try to create their own ratings) on sheet #2.
All Tourney Team:
|1.93||0.00||1.93||1||54.9||1||Kennedy Meeks||North Carolina||6||26.2||12.2||11.5||0.5||0.7||2.2||0.7||2.3||64.0%|
|1.82||0.08||1.74||3||53.1||2||Sindarius Thornwell||South Carolina||5||35.6||23.6||7.0||2.4||2.8||1.2||2.8||2.2||62.4%|
|1.09||0.00||1.09||6||20.6||21||Luke Maye||North Carolina||6||16.2||8.7||5.3||1.2||0.3||0.2||0.3||1.3||57.8%|
|1.08||0.00||1.08||7||34.7||6||Justin Jackson||North Carolina||6||34.5||19.5||5.2||3.7||2.0||0.5||2.0||1.5||54.7%|
|1.00||0.00||1.00||8||29.8||8||Derrick Walton Jr||Michigan||3||37.3||18.7||5.7||8.3||1.7||0.3||1.7||2.7||65.1%|
Kennedy Meeks is undoubtedly the MOP, looking at the tourney as a whole. Tyler Dorsey and Sindarius Thornwell were the top players who didn’t win it all.
As it is – have fun with the spreadsheet if you are so inclined. All the raw player data is there, per game and compiled – if you want to create something yourself. You can sort to your heart’s content, looking at favorite teams or players. You can even look at the very worst players of the tourney if you are more of a Debbie Downer. It’s all there, 694 players and 1295 individual player games. Enjoy.