My starting point was to look at the top ten outside hitters nationally in hitting percentage (this, and all other, rankings and statistics reported here are from roughly the end of March). These players were: Thomas Jaeschke (Loyola-Chicago, .383); Aaron Russell (Penn State, .379); Tamir Hershko (UC Irvine, .376); Josh Taylor (Pepperdine, .366); Cody Caldwell (Loyola, .325); Nicolas Szerszen (Ohio State, .322); Jonathan Martinez (Pfeiffer, .318); Jon Schaefer (Grand Canyon, .315); Alex Harthaller (IPFW, .305); and Enzo Mackenzie (Sacred Heart, .303).
Spiking may be the primary skill expected of outside hitters, but not the only one. They would also be expected to block, pass (on serve-receipt and in the middle of rallies), and serve. In the far right column of most box scores, one sees the heading "Points," which, for each player, shows the total number of points directly scored via kills, stuff blocks, and service aces. Though it encompasses multiple skills, I have never made much of the "points" statistic, as it does not reflect the number of points a player has cost his or her team through five possible types of errors: in attacking (hitting balls out of bounds or getting stuffed), serving, receiving serve, ball-handling, and blocking (i.e., touching the net).
To determine my vote for Outside Hitter of the Year, I am introducing a variation on the usual points statistic, which represents points earned minus points lost. I am calling my new statistic "Points Profit." (I could have called it "Net Points," reflecting "net" in the accounting sense, but such a term could easily be confused with points won at the net on a volleyball court.)
Using the top ten outside hitters nationally in hitting percentage (listed above), my plan was to compute Profit Points for each OH from his five toughest matches of the season. Match difficulty was based on opponent's rankings and match locations. The top ten teams (RPI) as of when I began the analyses were: 1. Loyola-Chicago; 2. UC Irvine; 3. Lewis; 4. Hawai'i; 5. Ohio State; 6. Penn State; 7. USC; 8. Pepperdine; 9. Indiana-Purdue Fort Wayne; and No. 10 BYU.
As an example, the five toughest matches for Loyola's Thomas Jaeschke were at Lewis, hosting Lewis, at Ohio State, at Penn State, and hosting Penn State. At Lewis, Jaeschke earned 21 points and cost the Ramblers 14, for a "profit" of 7. Each contending OH's five toughest matches and how he performed in them are shown in the following chart, which you may click to enlarge.
Although the candidate pool was drawn from the top ten national leaders among outside hitters in hitting percentage, only eight OH's were analyzed. Martinez was excluded because Pfeiffer played only one match against a top ten opponent (Ohio State). In addition, Grand Canyon's Schaefer missed substantial action in his team's most difficult matches, so he was likewise excluded. Sacred Heart's Mackenzie played in four of his team's five toughest matches, so I prorated his four-match total by multiplying it by (5/4) to estimate what it would have been had he played all five matches.
As shown in the above chart, the top three finishers in total Profit Points -- and for whom I cast my votes -- were Jaeschke (46 Profit Points, 1st); Hershko (43.5, 2nd); and Russell (42.5, 3rd).
Interestingly, even though hitting percentages (just one skill, from all matches up until late March) and Profit Points (multiple skills, in just five matches) were defined very differently, they had a near-perfect correlation (r = .93), where 1.00 is the maximum possible (see plot below). I'll have to conduct further studies to decide whether the extra work of calculating Profit Points is worth it, when hitting percentage is readily available. However, incorporating multiple skills allowed Hershko (third in hitting percentage) to leapfrog Russell (second in hitting percentage).
Click here for correlation plotter.
1 comment:
I find that a point scoring ratio is better than a points difference (or profit). This is because two players may have the same points profit but vastly different ratios. For example, a player may have scored 20 points but lost 10 points in errors, giving a +10 profit. However, another player may have scored 100 points but lost 90 points in errors, giving the same +10 profit. Clearly the first player has a better ratio because he scored two points for every error (=20/10 = 2) whereas the second player scores 1.11 points for every error (=100/90=1.11).
And a big reason for the correlation between hitting % and points profit is mainly because most points won and lost is via the attack so it makes sense the better the attacker, the more points profit that player will score. Unless that player is a particularly good/ bad server/ blocker/ receiver then it should be closely associated.
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