The upstarts and letdowns of 2007
With spring training right around the corner, everybody will be picking their candidates to surprise and disappoint in the coming year. While the exact definitions of “breakout” and “collapse” can be debated, I have a simple method: Guess what a player will do next year, and if he does substantially better, he was a surprise; if he does substantially worse, he was a disappointment.
In that spirit, let’s take a look back at last year’s over- and underperformers among hitters. We’ll compare each player’s actual performance with that predicted by the THT 2007 preseason book. The difference could be due to luck, skill change, or just a crummy projection (you can decide that last one for yourself).
We ran this exercise at the All-Star break if you’d like to compare the names here to those on the midyear lists.
Average
Batting average (>250 PA) Player Actual Projected Diff Matt R Kemp .342 .254 +.088 Magglio Ordonez .363 .290 +.073 Hanley Ramirez .331 .259 +.072 Jorge Posada .337 .267 +.070 James A Loney .331 .268 +.063 Norris S Hopper .328 .268 +.060 Dmitri Young .319 .261 +.058 Matt Diaz .337 .282 +.055 Ryan Ludwick .267 .213 +.054 Mike Lowell .324 .270 +.054 ... Adam Kennedy .218 .269 -.050 Dave Ross .202 .253 -.050 Andruw Jones .222 .273 -.050 Lyle Overbay .240 .291 -.051 Carlos J Quentin .213 .265 -.051 Jason Kendall .226 .283 -.056 Nick Punto .209 .270 -.060 Dioner F Navarro .226 .288 -.061 Jamey Carroll .224 .288 -.063 Ray Durham .217 .287 -.069
If we polled the fan population to choose players who had out-of-the-blue awesome years in 2007, I suspect that Jorge Posada, Magglio Ordonez, Dmitri Young and Mike Lowell would all be named. All outdid their projected batting averages by wide margins. While batting average is a skill prone to fluctuation—a few seeing-eye grounders here, a bloop over the infield there—it drives the value of many hitters.
Take Magglio Ordonez: Had he hit .290 as his projection prophesied and produced extra-base hits in the same ratio as he did, his seasonal line would have been .290/.370/.475—good production, even for a corner outfielder, but nothing close to his MVP-type real-world batting line of .363/.434/.595. Similarly, Norris Hopper would have gone from a highly acceptable .329/.371/.388 to an optioned-back-to-Triple-A .268/.329/.316. Andruw Jones’ .222 BA cost him big bucks—had he hit .273, he would have had an .860 OPS, 32 home runs, and enough money to buy a solid gold house and a rocket car. Instead, he’ll have to settle for just the rocket car.
Now, that’s not to say that these players didn’t “deserve” their high or low batting averages (though it’s always worth checking out their batted-ball distribution at our stat pages). After all, they put bat on ball and ended up on base instead of in the dugout (or not). It’s just that hitters provide a great deal of value when they hit for high averages, and predicting batting averages is a tough thing to do, indeed. With some of these players, you can argue that they are young and improving (Hanley Ramirez, Matt Kemp); with others, that they are old and cooked (Jason Kendall, Ray Durham). But sometimes, it’s just inexplicable.
BABIP
Batting average on balls in play (>250 PA) Player Actual Projected Diff Matt R Kemp .410 .301 +.109 Jorge Posada .386 .295 +.090 B.J. Upton .393 .310 +.082 Norris S Hopper .367 .290 +.077 Chone Figgins .390 .317 +.072 Magglio Ordonez .381 .308 +.072 Nook Logan .358 .288 +.069 Howie Kendrick .380 .314 +.066 Matt Diaz .378 .317 +.060 Reggie G Willits .357 .297 +.060 ... Nick Punto .255 .307 -.052 John R Buck .242 .295 -.053 Lyle Overbay .270 .327 -.057 Ray Durham .232 .295 -.062 Dave Ross .225 .290 -.065 Dioner F Navarro .249 .317 -.067 Jason Kendall .240 .309 -.068 Adam Kennedy .237 .308 -.070 Jamey Carroll .252 .325 -.072 Richie Sexson .216 .290 -.073
Presented without comment. Well, one: This is not actual BABIP versus “expected” BABIP based on 2007 batted-ball data; it’s actual BABIP versus projected BABIP, with the projection based on regressed batted-ball data prior to the 2007 season. Make of this what you will.
Power
Isolated slugging (>250 PA) Player Actual Projected Diff Carlos Pena .344 .164 +.180 Prince G Fielder .329 .217 +.112 Matt Stairs .260 .165 +.095 Hanley Ramirez .230 .141 +.089 Jimmy Rollins .234 .149 +.085 Alex Rodriguez .331 .246 +.085 B.J. Upton .208 .124 +.084 Franklin R Gutierrez .206 .127 +.079 Curtis Granderson .250 .173 +.077 Josh Fields .235 .160 +.075 ... Jacque Jones .114 .189 -.074 Javier Valentin .111 .186 -.074 Trot Nixon .084 .163 -.078 Manny Ramirez .196 .276 -.079 Albert Pujols .240 .321 -.080 Scott Rolen .132 .217 -.084 Nomar Garciaparra .088 .177 -.088 Jim Edmonds .150 .245 -.094 Travis Hafner .185 .287 -.101 Frank Thomas .203 .308 -.104
Whoa. Some guys with very good track records just didn’t get the job done this year. Frank Thomas, Travis Hafner and Manny Ramirez are some of the most powerful hitters in the league, and all saw a big drop-off in their isolated slugging. Age? Injury? Fluke? You be the judge. On the other hand, Prince Fielder and Alex Rodriguez upped their already considerable power to lofty heights. And look here: The MVPs of both leagues registered big power spikes in their historic years. Take note, aspiring ballplayers: If you want to win the MVP, the first rule is to be on a playoff team; the second rule is to hit the ball far.
Moreso than the aforementioned Posada, Ordonez, Young and Lowell, Carlos Pena almost inarguably had the biggest “Where the heck did that come from?” season. In what was looming at his last chance in the majors (signed to a minor league contract by the Devil Rays!), Pena blasted 46 bombs, won Comeback Player of the Year, and was rewarded with a shiny three-year contract.
Can he continue to hit home runs at that pace? I don’t know. But his homers weren’t cheapies. According to Hit Tracker Online, home runs can be classified as having “Just Enough,” “Plenty,” or “No Doubt” distance. The league-average distribution is 27%/55%/18%, respectively. Pena’s distribution? 23%/56%/21%. I would have guessed that the player with the biggest “how did that happen?” factor would have had his production driven by batting average. That was certainly not the case in the 2007 AL.
Patience
Walks per plate appearance (>250 PA) Player Actual Projected Diff Rickie Weeks .154 .082 +.071 Carlos Pena .168 .112 +.055 Pat Burrell .190 .140 +.049 Grady Sizemore .135 .086 +.048 Jose Reyes .100 .056 +.044 Jack Cust .207 .166 +.040 Ryan J Howard .165 .125 +.039 David A Wright .132 .094 +.038 Brian Roberts .124 .086 +.037 Magglio Ordonez .111 .075 +.036 ... Ryan Freel .059 .094 -.035 Mike Sweeney .058 .095 -.036 Ivan Rodriguez .017 .054 -.037 Jason Kendall .038 .080 -.041 Jason Giambi .132 .173 -.041 Mike Piazza .054 .099 -.044 Carlos Delgado .085 .130 -.044 Jim Edmonds .099 .144 -.044 Bobby Abreu .120 .166 -.046 Marcus Thames .045 .094 -.048
Is it good or bad when a player walks up a storm? As a fan of lead-footed outfielders with bad gloves, I was an avid follower of Jeremy Giambi back in the day. In 210 plate appearances with the Phillies in 2002, Giambi walked an incredible 52 times! A year later, he had fallen off the big-league map.
I don’t want to be accused of being a 1999-era stathead, but take a look at the players who outdid their projected walks: Those were some of the most valuable players in the big leagues last year. Now look at the disappointing players whose walk rates collapsed. I know, I know—walks aren’t the be-all and end-all. They don’t drive in runs. They don’t advance runners. Guys who walk with runners in scoring position ought to have their manliness questioned by sports radio commentators. Et cetera. Still… those are pretty compelling lists.
Is it possible that some of the positive differences emerge from a Jim Rice-ian fear factor? It could just be that as guys become better hitters, pitchers elect to throw less-hittable pitches, which in turn leads to more walks than might be expected. And while he was a great player in his halcyon days, which pitcher is afraid to throw a strike to Jason Kendall? Pitchf/x gurus, this could be an interesting thing to look at.
Contact
Strikeouts per plate appearance (>250 PA) Player Actual Projected Diff Craig Biggio .201 .143 +.058 Craig Monroe .252 .194 +.057 B.J. Upton .281 .225 +.055 Brandon Inge .259 .205 +.054 Jack Cust .323 .270 +.052 Brad Ausmus .186 .135 +.050 Carlos J Quentin .205 .156 +.049 Jonny Gomes .319 .271 +.048 Carl Crawford .178 .131 +.046 Jay Gibbons .179 .133 +.045 ... Jacque Jones .141 .201 -.059 Javier Valentin .094 .154 -.060 Dmitri Young .145 .206 -.060 Kevin Mench .068 .132 -.064 Mike A Napoli .239 .305 -.066 Tony F Pena .145 .212 -.067 Jose A Bautista .164 .236 -.072 Austin Kearns .157 .230 -.073 Corey Patterson .129 .207 -.078 Ryan Ludwick .212 .315 -.103
Strikeouts are a funny thing. From a value perspective, they’re not that much worse than other outs, save for the whole moving-runners-along thing. But a high number of strikeouts can portend bad things—just ask Jay Gibbons, Craig Biggio, Craig Monroe and Jonny Gomes, each of whom may not be long for the majors.
And young hitters like Carlos Quentin and B.J. Upton would do well to cut down on their strikeouts. According to Tangotiger, young hitters strike out a lot, cut their strikeouts as they reach their peak years, and then go back to striking out more frequently. Presumably, the dip in strikeouts through their peak years is a result of their tools matching their skills, while the uptick as they age comes from their physical tools failing them. Maybe that’s why guys who are entering their primes, such as Mike Napoli and Austin Kearns, appear at the bottom of this list. Maybe.
So which players are going to over- or underperform their projections in 2008? I don’t know, and I won’t dare guess. But if you like to go a-predicting, Tangotiger will take you on. Try your luck if you’re the risk-taking type!
References & Resources
Hey, speaking of projections, did you know the THT Season Preview 2008 is coming out soon? I hear from unbiased sources that it’s awesome, and it’s chock full of projections utilizing the latest technology in batted-ball data and statistical alchemy.