About CrumsRevenge

Former D1 scholly athlete. Former coach. I make Videos, take pictures, and obsess all things Louisville. If you are a Card-o-Holic, you can follow me here: Twitter - @CrumsRevenge; Instagram - CrumsRevenge.

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Post Season TVT: “Who Are These Cards?”

We have hit 5 post season games, so I am kicking out a TVT to show how this team has re-defined itself.  Literally overnight they have turned their season long production averages upside down.

TVT:  net positive production per minute.  Add up the good stuff, subtract the bad stuff, divide by minutes played.  Breaks every player down to a single number – “When you play for 1 minute, you gain the cardinals “x” net positive contributions per minute.  If you had a score of 1.0, that would be 1 net positive stat per minute.  that is hard to do.  .40, means you contribute a net positive stat in just over 2 minutes.


The most insane change in production? Team average has sky-rocketed.

  • Overall Season TVT- team average: 0.51 production per min
  • OOC schedule TVT – team average: 0.53 production per min
  • Conference Season TVT – team average:  0.48 production per min

Pretty Consistant right?  Look at the number below.  Insane.

  • Post season average TVT – team average:  0.65 production per min

Comparison graph by Category + Last five games by individual



  • All season long the top 3 has been: Dieng, Smith, Behanan – look who is crashing the party in the post season.  Answer: everyone. 
  • SVT is benefitting from a 2 minute game where he had 2 rebounds.  He is higher than he should be, but per minute – its a true stat.  He certainly is providing great minutes and nobody would disagree.
  • Behanan is down 30+% from his season averages, but he is consistantly delivering on his new average.  Could be a factor of others steal production because of our insanely high team average.  I am sure he wil lhave a big game soon, Chane doesn’t stay quiet for long.
  • The most telling stat of how good this team is playing is this; Luke Hancock is LAST on teh team in production per minute, and we know how happy we are with his play.  The team is on a level he haven’t seen all year and one we knew it could reach.

Game prediction:  Cards 78, Ducks 71.  Pitino challenges Dieng on the glass, he responds.  Dieng goes for double digit rebounds (he has been only averaging 5 in NCAA so far).

Go Cards.




NCAA: Conference Scorecard

Which conferences are hot at the right time?

I break it down round by round, and again for a total score all rounds.

Scoring: I had to change scoring.  I intended it to be a “per team” value, since some conferences have 8 bids and others 2.  Trying to avoid sheer volume from driving the winner. Scoring in a nutshell; add up the wins, give a bonus if an upset win, subtract points for an upset loss, and divide score by how many teams are playing in that round.

What I show

1. Raw totals:  bids, wins, losses, and % of upsets (win or lose).  One thing I like to look at is wins per bid.  The Raw total will show ho many total wins so far int he tourney, and you can see if bids are pulling their weight. Example:  A-10 has 7 wins from 5 bids.  Mountain West has 2 wins from their 5 bids.

2.  Conference Score Card Cumulative:  A-10 was the big winner in RD64 (6-0), but in the RD 32 they went 1-5, with a pair of upset losses. Their total score will be adding both rounds point totals together. As teams advance (or disappear) the point totals change.  In RD32, Pac-12 had only 2 wins, but they were both upset wins, so that is factored into their score.

3.  Per Round Performance:  How the selected conferences did per round.  I didn’t grab all conferences.  CUSA and MVC were left off, MVC did the best between those 2.

Another metric to watch:  Another thing to note is “% of the field”.  This helps give you an idea of how a conference is doing.  They get % of the field as bids, and they either maintain, or fall behind as the tourney progresses.  This will help stop the over generalized “they get all those bids, and all they do is lose” arguments. A conference cannot expect all their teams to advance to the Sweet 16, so this alongside Upset Win (UW%) / Upset Loss (UL%) help make sense of that.  Upset %’s are “when they win, X% are upsets wins”, and the same goes for losses.


Raw Totals (win/loss & Upset %’s).


Conference Score Card (RD 64 + RD 32)










Round 32 Resume


Round 64 Resume



Conference Report Card: RD 64, Tourney To Date, Ranking

A-10 huge winner in the round of 64 with 5 bids-6 wins, and 50% of their wins coming by upset.

Big East big loser with 8 bids – 3 wins, and 60% of their losses were via being upset.

It is a long tourney, so we will see how conferences fair each round and overall.  Does the Big East stink?  Nope, they have had 3 months to prove they win basketball games, and they do, but they certainly are having a bad round of 64 this week.

Raw: RD of 64 Results



Raw:  Tourney-to-Date

This will be the same as above this round, since only 1 round has been played. I added any “first 4” to this group (La Salle, Boise St).  Next round, this table will accumulate, as the above table will only be round specific.


Conference Rank

Add this, subtract that, and a little of this, and walaa – Big East, you dropped a bomb first round.  Time to make up for that in the coming rounds.  Sorted best rd of 64 to worst.

Let’s hope the Big East steps up in the Round of 32.  Game on.

Go Cards.