SoonerSpock
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I believe we sometimes rely too heavily on stats to predict winners and losers in team sports like softball. Stats are important, of course, but when rating individual players – and teams – I believe the eye test, the way that player or team is performing at this point in time, can be just as important.
Look throughout Division 1 and you'll find plenty of players with higher batting averages than Caleigh Clifton. But how many of those same players with their gaudy BAs can come close to CC's on-base percentage? RBIs are also important, but when a player pads her RBI total by hitting a grand slam with her team down 6-0 in the ninth inning, then are those RBIs really that meaningful?
Records against Top 25 teams? You want and need to beat highly ranked teams in order to have a good RPI, but a 2-1 loss early in the season might become a five-run win later in the year when your team is healthy and playing with a set lineup. I think we all know OU is a better team today than the team that lost 4-3 to Auburn in the season opener.
A pitcher has one really bad performance in 10 starts and gives up six earned runs before leaving the game in the fourth inning. Her ERA balloons over the 3.00 mark as a result. Does that mean she's likely to lose her next game to an opponent whose pitcher has an ERA under 1.50?
I believe there are probably only 6-7 teams that have a reasonable chance of winning the WCWS this year. Oklahoma is on that list, along with a couple of SEC teams, three from the PAC 12 and one from the Big 10. As you can probably guess, my picks have little to do with which teams have the best batting average, ERA or RPI.
No question the numbers do not provide the answer as to which team is the best team and which one will win a tournament. There are too many variables like injuries, teams on a roll, how teams match up, etc. However, quantitative statistical data is the best predictive vehicle for comparing teams strengths and weakness. Using the eye allows 10 different set of eyes to have 10 totally different evaluations with all evaluations being totally subjective.
The biggest shortcoming of the rpi analysis is the total disregard for margin of victory which needs to be a considered factor in determining SOS differences between team. Other methodologies have a margin of victory factor included.
Margin of victory has been excluded to ensure the rpi ranking system can not be used as a tool for the gaming industry and thereby has a greater error factor than other systems. For certain the statistical data including rpi cannot determine the winner of games but to me it is a better tool for making those comparisons than just a gut feeling from watching a few games as they use data from all games in their comparison with many systems making allowance for variables that change as the season progresses.
The below link is an analysis of 3 statistical ranking of the NCAA basketball tournament showing their methodology being accurate 63-72% of the time. Now give me some evidence that you can approach that accuracy without not once looking at any similar evaluations or statistical data prior to using your eye test to determine the winners of all the tournament games.
If you can beat those numbers I would recommend your moving to Vegas and changing vocations.
https://harvardsportsanalysis.wordpress.com/2013/02/19/rpi-prediction/
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