As discussed in the Improving the Indians Offense & Kenny Lofton post which focused on baserunning and the Improving the Indians Offense and Plate Discipline, the 2010 Indians offense was bad. The team was held to 2 runs or less 57 times posting a 4-53 record, had an additional 53 games with either 4 or 5 runs scored and posted a losing record of 21-32, and in the 55 games where they scored 5 or more runs and posted a record of 44-11. The team offense ranked 12th in the league in runs scored with 636 (3.99 R/G), 12th in batting average at .248, 10th in OBP at .322, 13th in SLG% at .378, and 13th in OPS at .700.

Two of the more popular prediction model systems for MLB are the CAIRO projection system presented at Replacement Level Yankees Weblog and Baseball Prospectus Pecota System. The CAIRO projection system has the Indians winning 71 games and the PECOTA system has the Indians winning 74 games. Can the Indians do better than that in 2011? If we start by assuming the pitching staff does not improve on its 2010 performance and allows the opposition to score 752 runs but the Indians improve the offense from 636 runs to a marginal 700 runs scored (as projected by PECOTA), the Indians could see a much improved record of 75-87 based on the Bill James Win Expectancy theory discussed in Improving the Indians Offense and Kenny Lofton.

The previous two articles on Improving the Indians Offense focused on some of the more non-traditional statistics of baserunning and contact/plate discipline. This edition is going to present the statistics of what was the outcome of making contact (Line Drive, Ground Ball, Fly Ball) and present some of the more traditional statistics for the projected 2011 Indians roster.

The three general outcomes when a batter makes contact are a line drive, a ground ball, or a fly ball. The following are two things to remember about batted ball stats as presented in the saberlibrary at fangraphs.com:

Two of the more popular prediction model systems for MLB are the CAIRO projection system presented at Replacement Level Yankees Weblog and Baseball Prospectus Pecota System. The CAIRO projection system has the Indians winning 71 games and the PECOTA system has the Indians winning 74 games. Can the Indians do better than that in 2011? If we start by assuming the pitching staff does not improve on its 2010 performance and allows the opposition to score 752 runs but the Indians improve the offense from 636 runs to a marginal 700 runs scored (as projected by PECOTA), the Indians could see a much improved record of 75-87 based on the Bill James Win Expectancy theory discussed in Improving the Indians Offense and Kenny Lofton.

The previous two articles on Improving the Indians Offense focused on some of the more non-traditional statistics of baserunning and contact/plate discipline. This edition is going to present the statistics of what was the outcome of making contact (Line Drive, Ground Ball, Fly Ball) and present some of the more traditional statistics for the projected 2011 Indians roster.

The three general outcomes when a batter makes contact are a line drive, a ground ball, or a fly ball. The following are two things to remember about batted ball stats as presented in the saberlibrary at fangraphs.com:

*- A line drive produces 1.26 runs/out, while fly balls produce .13 R/O and groundballs produce .05 R/O. In other words, batters want to hit lots of line drives and fly balls, while pitchers want to make batters hit groundballs.*

*- Players that don’t hit many balls in the air (higher GB% with lower FB% and LD%) generally have higher BABIPs and batting averages, but they have limited power.*Jason Donald led the Indians in hitting line drives at 21.6% and the worst was Lou Marson at 15%. Lou Marson led the Indinas in hitting groundballs at 56% and Austin Kearns hit the fewest at 28.6%. Austin Kearns and Shelly Duncan each led the team in flyballs at 55.8% while Lou Marson struggled to get the ball in the air at 29%.

The following table will include some of the more traditional statistics along with some maybe not so familiar stats such as OPS+, wRC, and wOBA. I like to look at a teams offense based on having five players with over 500 PA with a .340 wOBA or higer. Shin-Soo Choo was the only Indian to meet that criteria. Travis Hafner would of easily been but was only able to have 464 PA. Another item to look at is batting average on balls in play (BABIP), which simply put shows how lucky a hitter was or unlucky. The Major League average for BABIP was .297. The players who were unlucky in 2010 were the most unlucky were Michael Brantley (.271), Matt LaPorta (.250), Luis Valbuena (.238), and Lou Marson (.234). The luckiest hitters on the team were Shin-Soo Choo (.347), Travis Hafner (.332), Austin Kearns (.341). A brief description, again borrowed from the saberlibrary at fangraphs.com is provided after the table:

*On-base Plus Slugging Plus (OPS+)*has not gained as much widespread acceptance, but is a more informative metric than OPS. This statistic normalizes a player’s OPS – it adjusts for small variables that might affect OPS score (e.g. park effect) and puts the statistic on an easy-to-understand scale. A 100 OPS+ is league average, and each point up or down is one percentage point above or below league average. In other words, if a player had a 90 OPS+ last season, that means their OPS was 10% below league average. Also, since OPS+ adjusts for league and park effects, it’s possible to use OPS+ to compare players from different years and on different teams.*Weighted Runs Created (wRC)*is an improved version of Bill James’ old Runs Created (RC) statistic, which attempted to quantify a player’s total offensive value and measure it by runs. This way, instead of looking at a player’s line and listing out all the details (e.g. 23 2B, 15 HR, 55 BB, 110 K, 19 SB, 5 CS), you could synthesize all the information into one metric and say, “Player x was worth 24 runs to his team last year.” While the idea was sound, James’ formula has since been superseded by Tom Tango’s wRC and is based off of**wOBA**.*Weighted On-Base Average (wOBA)*is based on a simple concept: not all hits are created equal. Batting average would have you believe they are, but think about it: what’s more valuable, a single or a homerun? Batting average doesn’t account for this difference and slugging percentage doesn’t do so accurately (is a double worth twice as much as a single? In short,**no**). OPS does a good job of combining all the different aspects of hitting (hitting for average, hitting for power, having plate discipline) into one metric, but it weighs slugging percentage the same as on-base percentage, while on-base percentage is more valuable than slugging. Weighted On-Base Average combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value.- The average BABIP for hitters is around .290 to .310. If you see any player that deviates from this average to an extreme, they’re likely due for regression. However, hitters can influence their BABIPs to some extent. For example, speedy hitters typically have high career BABIP rates (like Ichiro and his .357 career BABIP), so don’t expect all players to regress to league average – instead, look at a player’s career BABIP rate.

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