And here we are, the Bonds game I mentioned earlier is only 18th all time on the hitting list. The greatest game was by Art Shamsky, on August 12, 1966. It’s even more amazing because Shamsky only came into the game at the top of the 8th pinch hitting for the pitcher and then moving to left field. In the 8th he hit a 2 run homerun to give the Reds a 1 run lead. Pittsburgh tied it in the 9th, and then took the lead in the top of the 10th. In the bottom of the 10th Shamsky hit another home run to retie the game at 9 all. Once again the Reds pitcher failed to deliver, and the Reds were down 2 in the bottom of the 11th. ONCE AGAIN Shamsky hits a home run, another two run shot, and the game is again tied!

Sadly the game did not have a happy ending for Shamsky, as 6 of the next 7 Reds failed to get a hit, Pittsburgh scored the winning runs in the top of the 13th, and the game ended with the #7 batter hitting into a double play (Shamsky was batting 9th).

So 3 ABs for a WPA of 1.50. And in a losing effort, no less.

]]>The game you’ve mentioned is at http://www.baseball-reference.com/boxes/LAN/LAN200609180.shtml#wpa . You can see that San Diego had a 98% chance of winning before those home runs. It fell to 95%, then 90%, then 78%, then 35% after each of those home runs. Even so, they were back up to a 65% chance of winning before Nomar hit that final home run.

Apparently you can use the Play Index to find top WPA performances (according to http://www.baseball-reference.com/blog/archives/4718 ), but I must be doing it wrong because I can’t get it to work.

]]>However, this is less interesting when you consider that the “differences,” that is, the slope between each discrete time, is simply the WPA of that event.

A way to measure how this aggregates over the game would be to take the absolute values of the WPAs (that is, add both teams’ WPA positively). The higher the sum, the more “back-and-forth” there was in the game. If the sum is low (say, close to 1), then the game was simply a steady march to a seemingly inevitable conclusion.

]]>You aren’t operating in continuous space, a discrete model for the derivatives is appropriate, and easy. Otherwise you are arbitrarily adding bias based on the method of curve fitting… no?

]]>(Gee, if one of my students had done that…)

]]>I think there are two types of drama in a game, as exemplified by the two games I discussed. There is the tight close game, typically a pitching/defense dominated game, like the WN/ATL affair. In these games the tension or drama is high the entire time, and well displayed graphically by the LI graph below the main WP graph. In this case, the LI is probably the best indicator or tension.

The other time of game is the rollercoaster that was exemplified by Game 163. In that game the LI graph has more ups and downs as there were 6 win/loss/tie changes so a fan of one of the Twins or Tigers has more emotional ups and downs, which is a different type of excitement. Either the mean absolute WPA [MA(WPA)?] or the RMS(WPA) do a better job of capturing this type of tension. I’m still trying to decide which of these I like better,

I guess I’m simply trying to derive a single number measure that captures each of the two pictures: aLI or my adjusted version, captures the story on the LI graph, while RMS(WPA) or MA(WPA) captures the story of the Win Probability graph.

Finally, my measures differ by dividing by 54, which normalizes these measures to a minimum 9-inning game as the more plays there are, the more entertainment value. That is probably biased against a game in which both pitchers are posting a no-no which gets decided in the last at bat.

]]>The higher the LI, the higher the “drama”. The the closer to .500 the average win expectancy is, the “tighter” the game.

I would say that if someone wants to introduce something new, use those as the baseline benchmarks, and then tell us what your method shows that these two metrics don’t.

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