The Value of MWS and Reverse Boxscores

Who was responsible for the Milwaukee Bucks loss to the Atlanta Hawks?

If you examine the box score it appears as though G John Salmons was not responsible at all.  It appears as though he had a very nice game. 

On the other hand, if you look at F Carlos Delfino’s box score numbers, it appears as though he had a mediocre game. 

Both appearances are completely untrue! 

I went through the NBA’s “Play-by-Play” and used that, along with my notes about who covered whom, and took notes assigning Hawk statistics to their Buck counterpart.  

John Salmons had a HORRIBLE basketball game.  His defense on any Hawk, specifically Joe Johnson, was non-existent. 

John Salmons counterparts produced a Win Score of +17.0 in the 43.5 minutes he was on the court.  Salmons mostly played shooting guard during those minutes.  The average shooting guard in the NBA produces a Win Score of around +6.3 per 48 minutes.   Can you see the damage Salmons’ counterparts did?

Now take Carlos Delfino.  At first glance Delfino had an “eh” performance.  Not so.

Somehow Delfino normally shuts guys down on the opponent offensive end of the court, and he normally produces very efficient statistics for the Bucks on their end. 

That was the case in the Hawk game.  Delfino covered, mainly, SF Marvin Williams and PF Josh Smith (Scott Skiles went to a small lineup in the fourth quarter that featured Delfino at power forward — as I predicted).  He was quite successful in preventing those players from producing efficient statistics.

When Carlos Delfino was on the court, his counterpart opponents were essentially non-productive, producing a Win Score of 1.0.  The average NBA player who plays 75% small forward and 25% power forward would produce a Win Score of around 8.4 in 48 minutes of action.  Delfino played 35 minutes, so the average SF/PF mixture specified above would have been expected to produce around a 6.2 Win Score. 

In other words, either Delfino is very lucky, or he completely smothered his Hawk cover.  In my eyes, that made it far more likely the Milwaukee Bucks would win the basketball game.

And when you combine the fact that Delfino produced a Win Score of +10.5… well, he had a magnificent game.  (You look at his box score and you go “How is that a +10.5”.  Here’s how he does it, night after night.  First, he produced 12 points in only 8 offensive possessions.  +4.0.  Then he created 6 possessions with 4 rebounds and 2 steals.  +6.0.  Then he helped other Bucks score 4 times.  +2.0.  Then he stamped one Hawk shot “Return to Sender”.  +0.5.  So we are through the positives and we are at +12.5.  Now the negatives.  He gave back one possession.  -1.0.  And he was naughty and got caught twice.  -1.0.  There you have it:  Win Score of +10.5.  Unbelievably clean and efficient performance.)

Here’s what I’m driving at with this post.  Reverse Boxscores, while imperfect, would still be extremely valuable pieces of information.  After all, unless you are a woman in Iowa, basketball is played on two ends of the court.  Its unbelievable to me that something like Reverse Boxscores wasn’t in place many years ago.       

Here’s an example of why it doesn’t exist.  

The Basketball Geek somehow has the technology in place to evaluate players on the defensive end.  I asked him if he could produce simple reverse statistics for NBA players.  He replied by giving me a lecture on the complex definition of “defense” in basketball, and basically indicated that his interest lies in finding a mathematical formula that perfectly distributes defensive responsibility.

Do you see the problem here?  Its the whole concept of fairness, intertwined with the notion of “defense”, made more difficult by basketball’s uncertain “player positioning”.  Those that could produce reverse statistics are reluctant to do so because the statistics might assign responsibility to the wrong party.  In other words, the “perfect” is holding us back from the “good”.           

We need to get off the whole notion of “defense”.  Basketball games are won and lost based upon comparative production.  Given basketball’s clear and established distribution of labor, it makes sense to compare player’s of similar height/weight distribution.   

That’s why reverse statistics are so important.  Yes, there are things a player cannot control that reverse statistics “assigns” him responsibility for.  But we are humans.  We can discern such things.

But gives us the information so we can do so.



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