Forecaster's Toolbox: Batters

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Batting Eye, Contact and Batting Average

Batting average (BA, or Avg)

This is where it starts. BA is a grand old nugget that has long outgrown its usefulness. We revere .300 hitting superstars and scoff at .250 hitters, yet the difference between the two is one hit every 20 ABs. This one hit every five games is not nearly the wide variance that exists in our perceptions of what it means to be a .300 or .250 hitter. BA is a poor evaluator of performance in that it neglects the offensive value of the base on balls and assumes that all hits are created equal.

Walk rate (bb%) 

(BB / (AB + BB)) 

A measure of a batter’s plate patience. BENCHMARKS: The best batters will have levels more than 10%. Those with poor plate patience will have levels of 5% or less.

Walk rate and batting average  (Patrick Davitt)

Analysts have long told us that a hitter’s walk rate (bb%) is a reliable leading indicator of batting average (BA), and that changes in bb% are clues about expected improvements or declines in BA. This was probably because analysts used bb% as a proxy for a hitter’s ability to “be selective” by laying off pitches outside the zone and swinging at pitches in the zone.

While the idea makes intuitive sense, a review of several seasons’ bb% and BA data showed that bb% and BA are as unconnected as they could be. In any single season, and for all seasons in the study combined, the overall correlation between the two variables was +0.01 (a score of 0.00 means two variables are completely uncorrelated).

For any given bb%, BAs always clustered around .250, with most in a range of about .240 to .260. Minimum and maximum BAs for any decile of bb% were likewise random. And even at BA extremes, the bb% was not correlated; among .300+ hitters, for example, bb% ranged from 4% to 14%).

On base average (OB)

(H + BB + HBP) / (AB + BB + HBP + Sac Flies)

Addressing a key deficiency with BA, OB gives value to events that get batters on base, but are not hits. An OB of .350 can be read as “this batter gets on base 35% of the time.” When a run is scored, there is no distinction made as to how that runner reached base. So, two-thirds of the time—about how often a batter comes to the plate with the bases empty—a walk really is as good as a hit. BENCHMARKS: We know what a .300 hitter is, but what represents “good” for OB?  That comparable level would likely be .400, with .275 representing the comparable level of futility.

Ground ball, line drive, fly ball percentages (G/L/F)

The percentage of all balls in play that are hit on the ground, as line drives and in the air. For batters, increased fly ball tendency may foretell a rise in power skills; increased line drive tendency may foretell an improvement in batting average. For a pitcher, the ability to keep the ball on the ground can contribute to his statistical output exceeding his demonstrated skill level.

*BIP Type    Total%    Out%
Ground ball    45%    72%
Line drive     20%    28%
Fly ball       35%    85%
TOTAL         100%    69%
*Data only includes fieldable balls and is net of HRs.

Line drives and luck  (Patrick Davitt)

Given that each individual batter’s hit rate sets its own baseline, and that line drives (LD) are the most productive type of batted ball, a study looked at the relationship between the two. Among the findings were that hit rates on LDs are much higher than on FBs or GBs, with individual batters consistently falling into the 72-73% range. Ninety-five percent of all batters fall between the range of 60%-86%; batters outside this range regress very quickly, often within the season.

Note that batters’ BAs did not always follow their LD% up or down, because some of them enjoyed higher hit rates on other batted balls, improved their contact rates, or both. Still, it’s justifiable to bet that players hitting the ball with authority but getting fewer hits than they should will correct over time.

Batting eye (Eye)

(Walks / Strikeouts)

A measure of a player’s strike zone judgment. BENCHMARKS: The best hitters have Eye ratios more than 1.00 (indicating more walks than strikeouts) and are the most likely to be among a league’s .300 hitters. Ratios less than 0.50 represent batters who likely also have lower BAs. 

Batting eye as a leading indicator

There is a strong correlation between strike zone judgment and batting average. However, research  shows that this is more descriptive than predictive:

Batting Average

Batting Eye    2011    2012    2013    2014    2015
0.00 - 0.25    .232    .243    .242    .238    .243
0.26 - 0.50    .254    .255    .253    .253    .257
0.51 - 0.75    .267    .268    .265    .268    .267
0.76 - 1.00    .276    .276    .277    .270    .280
1.01 and over  .298    .292    .284    .304    .293

We have been running the above chart for years and have always had large enough samples to make each group statistically significant. But not the past two years. The last group—1.01 and over—contained only six players in 2014 and eight players in 2015. The correlation held, but the downward pressure on batting averages continues to change the game.

We can create percentage plays for the different levels:

For Eye             Pct who bat
Levels of         .300+      .250- 
0.00 - 0.25         7%        39%
0.26 - 0.50        14%        26%
0.51 - 0.75        18%        17%
0.76 - 1.00        32%        14%
1.01 - 1.50        51%         9%
1.51 +             59%         4%

Any batter with an eye ratio more than 1.50 has about a 4% chance of hitting less than .250 over 500 at bats.

Of all .300 hitters, those with ratios of at least 1.00 have a 65% chance of repeating as .300 hitters. Those with ratios less than 1.00 have less than a 50% chance of repeating.

Only 4% of sub-.250 hitters with ratios less than 0.50 will mature into .300 hitters the following year. 

In a 1995-2000 study, only 37 batters hit .300-plus with a sub-0.50 eye ratio over at least 300 AB in a season. Of this group, 30% were able to accomplish this feat on a consistent basis. For the other 70%, this was a short-term aberration.

Contact rate (ct%)

((AB - K) / AB)

Measures a batter’s ability to get wood on the ball and hit it into the field of play. BENCHMARKS: Those batters with the best contact skill will have levels of 90% or better. The hackers of society will have levels of 75% or less.

Contact rate as a leading indicator

The more often a batter makes contact with the ball, the higher the likelihood that he will hit safely.  

Batting Average

Contact Rate    2011    2012    2013    2014    2015
0% - 60%        .171    .197    .203    .176    .194
61% - 65%       .199    .226    .211    .217    .217
66% - 70%       .229    .231    .232    .230    .236
71% - 75%       .243    .252    .246    .243    .254
76% - 80%       .260    .255    .261    .257    .257
81% - 85%       .268    .268    .268    .266    .268
86% - 90%       .272    .278    .272    .276    .277
Over 90%        .290    .282    .270    .324    .284

Once again, the size of the highest-skilled group has dwindled, here to only 17 players in 2014 and 25 players in 2015. Last year, only four had more than 50 AB; this year only six with nearly half posting fewer than 10 AB. 

Contact rate and walk rate as leading indicators

A matrix of contact rates and walk rates can provide expectation benchmarks for a player’s batting average:

Walk rate (bb%)

                   0-5        6-10      11-15      16+
65-               .179        .195      .229      .237
66-75             .190        .248      .254      .272
76-85             .265        .267      .276      .283
86+               .269        .279      .301      .309
HCt and HctX  (Patrick Davitt)A contact rate of 65% or lower offers virtually no chance for a player to hit even .250, no matter how high a walk rate he has. The .300 hitters most often come from the group with a minimum 86% contact and 11% walk rate.

HCt= hard hit ball rate x contact rate

HctX= Player HCt divided by league average Hct, normalized to 100

The combination of making contact and hitting the ball hard might be the most important skills for a batter. HctX correlates very strongly with BA, and at higher BA levels often does so with high accuracy. Its success with HR was somewhat limited, probably due to GB/FB differences. BENCHMARKS: The average major-leaguer in a given year has a HctX of 100. Elite batters have an HctX of 135 or above; weakest batters have HctX of 55 or below.

Balls in play (BIP)

(AB – K)

The total number of batted balls that are hit fair, both hits and outs. An analysis of how these balls are hit—on the ground, in the air, hits, outs, etc.—can provide analytical insight, from player skill levels to the impact of luck on statistical output.

Batting average on balls in play  (Voros McCracken) 

(H – HR) / (AB – HR – K)

Also called hit rate (h%). The percent of balls hit into the field of play that fall for hits. BENCHMARK: Every hitter establishes his own individual hit rate that stabilizes over time. A batter whose seasonal hit rate varies significantly from the h% he has established over the preceding three seasons (variance of at least +/- 3%) is likely to improve or regress to his individual h% mean (with over-performer declines more likely and sharper than under-performer recoveries). Three-year h% levels strongly predict a player’s h% the following year. 

Pitches/Plate Appearance as a leading indicator for BA  (Paul Petera)

The art of working the count has long been considered one of the more crucial aspects of good hitting. It is common knowledge that the more pitches a hitter sees, the greater opportunity he has to reach base safely. 

P/PA            OBA      BA  
4.00+          .360     .264  
3.75-3.99      .347     .271  
3.50-3.74      .334     .274  
Under 3.50     .321     .276      

Generally speaking, the more pitches seen,the lower the BA, but the higher the OBA. But what about the outliers, those players that bucked the trend in year #1? 


                      BA Improved    BA Declined
Low P/PA and Low BA      77%           23%
High P/PA and High BA    21%           79%

In these scenarios, there was a strong tendency for performance to normalize in year #2.

Expected batting average  (John Burnson) 

xCT% * [xH1% + xH2%] where  xH1% = GB% x  [0.0004 PX + 0.062 ln(SX)] + LD%  x  [0.93 - 0.086 ln(SX)] + FB%  x  0.12 and

xH2% = FB%  x  [0.0013 PX - 0.0002 SX - 0.057] + GB%  x  [0.0006 PX]

A hitter’s batting average as calculated by multiplying the percentage of balls put in play (contact rate) by the chance that a ball in play falls for a hit. The likelihood that a ball in play falls for a hit is a product of the speed of the ball and distance it is hit (PX), the speed of the batter (SX), and distribution of ground balls, fly balls, and line drives. We further split it out by non-homerun hit rate (xH1%) and homerun hit rate (xH2%). BENCHMARKS: In general, xBA should approximate batting average fairly closely. Those hitters who have large variances between the two gauges are candidates for further analysis. LIMITATION: xBA tends to understate a batter’s true value if he is an extreme ground ball hitter (G/F ratio over 3.0) with a low PX.  These players are not inherently weak, but choose to take safe singles rather than swing for the fences. 

Expected batting average variance

xBA – BA 

The variance between a batter’s BA and his xBA is a measure of over- or under-achievement. A positive variance indicates the potential for a batter’s BA to rise. A negative variance indicates the potential for BA to decline. BENCHMARK: Discount variances that are less than 20 points. Any variance more than 30 points is regarded as a strong indicator of future change.  


Slugging average (Slg)

(Singles + (2 x Doubles) + (3 x Triples) + (4 x HR)) / AB

A measure of the total number of bases accumulated (or the minimum number of runners’ bases advanced) per at bat. It is a misnomer; it is not a true measure of a batter’s slugging ability because it includes singles. Slg also assumes that each type of hit has proportionately increasing value (i.e. a double is twice as valuable as a single, etc.) which is not true. For instance, with the bases loaded, a HR always scores four runs, a triple always scores three, but a double could score two or three and a single could score one, or two, or even three. BENCHMARKS: Top batters will have levels over .500. The bottom batters will have levels less than .300.

Fly ball tendency and power  (Mat Olkin)

There is a proven connection between a hitter’s ground ball/fly ball tendencies and his power production.

1.     Extreme ground ball hitters generally do not hit for much power. It’s almost impossible for a hitter with a ground/fly ratio over 1.80 to hit enough fly balls to produce even 25 HRs in a season. However, this does not mean that a low G/F ratio necessarily guarantees power production. Some players have no problem getting the ball into the air, but lack the strength to reach the fences consistently. 

2.    Most batters’ ground/fly ratios stay pretty steady over time. Most year-to-year changes are small and random, as they are in any other statistical category. A large, sudden change in G/F, on the other hand, can signal a conscious change in plate approach. And so...

3.    If a player posts high G/F ratios in his first few years, he probably isn’t ever going to hit for all that much power. 

4.    When a batter’s power suddenly jumps, his G/F ratio often drops at the same time. 

5.    Every so often, a hitter’s ratio will drop significantly even as his power production remains level. In these rare cases, impending power development is likely, since the two factors almost always follow each other.

Home runs to fly ball rate (hr/f)

The percent of fly balls that are hit for HRs.  

hr/f rate as a leading indicator  (Joshua Randall)

Each batter establishes an individual home run to fly ball rate that stabilizes over rolling three-year periods; those levels strongly predict the hr/f in the subsequent year. A batter who varies significantly from his hr/f is likely to regress toward his individual hr/f mean, with over-performance decline more likely and more severe than under-performance recovery.

Hard-hit flies as a sustainable skill  (Patrick Davitt)

A study of data from 2009-2011 found that we should seek batters with a high Hard-Hit Fly Ball percentage (HHFB%). Among the findings:

•    Avoiding pop-ups and hitting HHFBs are sustainable core power skills.

•    Consistent HHFB% performance marks batters with power potential.

•    When looking for candidates to regress, we should look at individual past levels of HR/HHFB, perhaps using a three-year rolling average.

Linear weighted power (LWPwr)

((Doubles x .8) + (Triples x .8) + (HR x 1.4)) / (At bats- K) x 100

A variation of the linear weights formula that considers only events that are measures of a batter’s pure power. BENCHMARKS: Top sluggers typically top the 17 mark. Weak hitters will have a LWPwr level of less than 10.

Linear weighted power index (PX)

(Batter’s LWPwr / League LWPwr) x 100

LWPwr is presented in this book in its normalized form to get a better read on a batter’s accomplishment in each year. For instance, a 30-HR season today is much more of an accomplishment than 30 HRs hit in a higher offense year like 2003. BENCHMARKS: A level of 100 equals league average power skills. Any player with a value more than 100 has above average power skills, and those more than 150 are the Slugging Elite.

Expected LW power index (xPX) (Bill Macey)

2.6 + 269*HHLD% + 724*HHFB%

Previous research has shown that hard-hit balls are more likely to result in hits and hard-hit fly balls are more likely to end up as HRs. As such, we can use hard-hit ball data to calculate an expected skills-based power index. This metric  starts with hard-hit ball data, which measures a player’s fundamental skill of making solid contact, and then places it on the same scale as PX (xPX). In the above formula, HHLD% is calculated as the number of hard hit-line drives divided by the total number of balls put in play. HHFB% is similarly calculated for fly balls.


Pitches/Plate Appearance as a leading indicator for PX  (Paul Petera)

Working the count has a positive effect on power. 

P/PA           PX      

4.00+          123  

3.75-3.99      108  

3.50-3.74       96  

Under 3.50      84  

As for the year #1 outliers: 


                        PX Improved    PX Declined
Low P/PA and High PX       11%             89%
High P/PA and Low PX       70%             30%

In these scenarios, there was a strong tendency for performance to normalize in year #2.

Doubles as a leading indicator for home runs  (Bill Macey)

There is little support for the theory that hitting many doubles in year x leads to an increase in HR in year x+1. However, it was shown that batters with high doubles rates (2B/AB) also tend to hit more HR/AB than the league average; oddly, they are unable to sustain the high 2B/AB rate but do sustain their higher HR/AB rates. Batters with high 2B/AB rates and low HR/AB rates are more likely to see HR gains in the following year, but those rates will still typically trail the league average. And, batters who experience a surge in 2B/AB typically give back most of those gains in the following year without any corresponding gain in HR.

Opposite field home runs  (Ed DeCaria)

From 2001-2008, nearly 75% of all HRs were hit to the batter’s pull field, with the remaining 25% distributed roughly evenly between straight away and opposite field. Left-handers accomplished the feat slightly more often than right-handers (including switch-hitters hitting each way), and younger hitters did it significantly more often than older hitters. The trend toward pulled home runs was especially strong after age 36.

    Power             Opp.        Straight    Pull
Quartile    AB/HR     Field         Away       Field
Top 25%    17.2        16%          16%         68%
2nd 25%    28.0        11%          12%         77%
3rd 25%    44.1         9%          10%          8%
Bot 25%    94.7         5%           6%         89%

Opposite field HRs serve as a strong indicator of overall home run power (AB/HR). Power hitters (smaller AB/HR rates) hit a far higher percentage of their HR to the opposite field or straight away (over 30%). Conversely, non-power hitters hit almost 90% of their home runs to their pull field. 

Performance in Y2-Y4 (% of Group)
Y1 Trigger    <=30 AB/HR    5.5+ RC/G    $16+ R$
2+ OppHR        69%           46%         33%
<2 OppHR        29%           13%         12%

Players who hit just two or more OppHR in one season were 2-3 times as likely as those who hit zero or one OppHR to sustain strong AB/HR rates, RC/G levels, or R$ values over the following three seasons. 

Y2-Y4 Breakout Performance
(% Breakout by Group, Age <=26 Only)
                  AB/HR            RC/G              R$
Y1 Trigger     >35 to <=30     <4.5 to 5.5+     <$8 to $16+
2+ OppHR        32%                21%              30%
<2 OppHR        23%                12%              10%

Roughly one of every 3-4 batters age 26 or younger experiences a sustained three-year breakout in AB/HR, RC/G or R$ after a season in which they hit 2+ OppHR, far better odds than the one in 8-10 batters who experience a breakout without the 2+ OppHR trigger.

Home runs in bunches (Patrick Davitt)

A study from HR data from 2010-2012 showed that batters hit HRs in a random manner, with game-gaps between HRs that correspond roughly to their average days per HR. Thus, the theory that batters hit HRs in “bunches” is a fallacy. It appears pointless to try to “time the market” by predicting the beginning or end of a drought or a bunch, or by assuming the end of one presages the beginning of the other, despite what the ex-player in the broadcast booth tells you.

Power breakout profile

It is not easy to predict which batters will experience a power spike. We can categorize power breakouts to determine the likelihood of a player taking a step up or of a surprise performer repeating his feat. Possibilities:

•    Increase in playing time 

•    History of power skills at some time in the past 

•     Redistribution of already demonstrated extra base hit power

•     Normal skills growth 

•     Situational breakouts, particularly in hitter-friendly venues 

•     Increased fly ball tendency 

•     Use of illegal performance-enhancing substances

•     Miscellaneous unexplained variables


Wasted talent on the base paths

We refer to some players as having “wasted talent,” a high level skill that is negated by a deficiency in another skill. Among these types are players who have blazing speed that is negated by a sub-.300 on base average.

These players can have short-term value. However, their stolen base totals are tied so tightly to their “green light” that any change in managerial strategy could completely erase that value. A higher OB mitigates that downside; the good news is that plate patience can be taught.

Players in 2015 who had at least 20 SBs with an OBP less than .300, and whose SB output could be at risk, are Billy Hamilton (57 SB, .274 OBP), Jean Segura (25, 281) and Jake Marisnick (24, .281). Note that Hamilton and Segura are returnees to this list from last year; however, that list contained six names, four of whom did not repeat.

Speed score  (Bill James)

A measure of the various elements that comprise a runner’s speed skills. Although this formula (a variation of James’ original version) may be used as a leading indicator for stolen base output, SB attempts are controlled by managerial strategy which makes speed score somewhat less valuable. 

Speed score is calculated as the mean value of the following four elements:

1. Stolen base efficiency = (((SB + 3)/(SB + CS + 7)) - .4) x 20

2. Stolen base freq. = Square root of ((SB + CS)/(Singles + BB)) / .07

3. Triples rating = (3B / (AB - HR - K)) and the result assigned a value based on the following chart:

    < 0.001    0       0.0105     6
    0.001      1         0.013    7
    0.0023     2        0.0158    8
    0.0039     3        0.0189    9
    0.0058     4        0.0223+  10
    0.008      5

4. Runs scored as a percentage of times on base =
(((R - HR) / (H + BB - HR)) - .1) / .04

Speed score index (SX)

(Batter’s speed score / League speed score) x 100 

Normalized speed scores get a better read on a runner’s accomplishment in context. A level of 100 equals league average speed skill. Values more than 100 indicate above average skill, more than 200 represent the Fleet of Feet Elite.

Statistically scouted speed (Spd)  (Ed DeCaria)

(104 + {[(Runs–HR+10*age_wt)/(RBI-HR+10)]/lg_av*100} / 5 + {[(3B+5*age_wt)/(2B+3B+5)]/lg_av*100} / 5 + {[(SoftMedGBhits+25*age_wt)/(SoftMedGB+25)]/lg_av*100} / 2 - {[Weight (Lbs)/Height (In)^2 * 703]/lg_av*100}

A skills-based gauge that measures speed without relying on stolen bases. Its components are:

•    (Runs – HR) / (RBI – HR): This metric aims to minimize the influence of extra base hit power and team run-scoring rates on perceived speed.

•    3B / (2B + 3B): No one can deny that triples are a fast runner’s stat; dividing them by 2B+3B instead of all balls in play dampens the power aspect of extra base hits.

 •    (Soft + Medium Ground Ball Hits) / (Soft + Medium Ground Balls): Faster runners are more likely than slower runners to beat out routine grounders. Hard hit balls are excluded from numerator and denominator.

•    Body Mass Index (BMI): Calculated as Weight (lbs) / Height (in)2 * 703. All other factors considered, leaner players run faster than heavier ones.

In this book, the formula is scaled as an index with a midpoint of 100.

Stolen base opportunity percent (SBO)

(SB + CS) / (BB + Singles)

A rough approximation of how often a baserunner attempts a stolen base. Provides a comparative measure for players on a given team and, as a team measure, the propensity of a manager to give a “green light” to his runners.

Roto Speed (RSpd)

(Spd x (SBO + SB%))

An adjustment to the measure for raw speed that takes into account a runner’s opportunities to steal and his success rate. This stat is intended to provide a more accurate predictive measure of stolen bases for the Mayberry Method.

Stolen base breakout profile  (Bob Berger)

To find stolen base breakouts (first 30+ steal season in the majors), look for players that:

•     are between 22-27 years old

•    have 3-7 years of professional (minors and MLB) experience

•    have previous steals at the MLB level

•    have averaged 20+ SB in previous three seasons (majors and minors combined)

•    have at least one professional season of 30+ SB

Overall Performance Analysis

On base plus slugging average (OPS)

A simple sum of the two gauges, it is considered one of the better evaluators of overall performance. OPS combines the two basic elements of offensive production—the ability to get on base (OB) and the ability to advance baserunners (Slg). BENCHMARKS: The game’s top batters will have OPS levels more than .900. The worst batters will have levels less than .600.

Base Performance Value (BPV)

(Walk rate - 5) x 2) + ((Contact rate - 75) x 4) + ((Power Index - 80) x 0.8) + ((Spd - 80) x 0.3)

A single value that describes a player’s overall raw skill level. This is more useful than traditional statistical gauges to track player performance trends and project future statistical output. The BPV formula combines and weights several BPIs.

This formula combines the individual raw skills of batting eye, contact rate, power and speed. BENCHMARKS: The best hitters will have a BPV of 50 or greater. 

Base Performance Index (BPX)

BPV scaled to league average to account for year-to-year fluctuations in league-wide statistical performance. It’s a snapshot of a player’s overall skills compared to an average player. BENCHMARK: A level of 100 means a player had a league-average BPV in that given season. 

Linear weights  (Pete Palmer) 

((Singles x .46) + (Doubles x .8) + (Triples x 1.02) + (Home runs x 1.4) + (Walks x .33) + (Stolen Bases x .3) - (Caught Stealing x .6) - ((At bats - Hits) x Normalizing Factor) 

(Also referred to as Batting Runs.) Formula whose premise is that all events in baseball are linear; that is, the output (runs) is directly proportional to the input (offensive events). Each of these events is then weighted according to its relative value in producing runs. Positive events—hits, walks, stolen bases—have positive values. Negative events—outs, caught stealing—have negative values.

The normalizing factor, representing the value of an out, is an offset to the level of offense in a given year. It changes every season, growing larger in high offense years and smaller in low offense years. The value is about .26 and varies by league.

LW is not included in the player forecast boxes, but the LW concept is used with the linear weighted power gauge.

Runs above replacement (RAR)

An estimate of the number of runs a player contributes above a “replacement level” player. “Replacement” is defined as the level of performance at which another player can easily be found at little or no cost to a team. What constitutes replacement level is a topic that is hotly debated. There are a variety of formulas and rules of thumb used to determine this level for each position (replacement level for a shortstop will be very different from replacement level for an outfielder). Our estimates appear below.

One of the major values of RAR for fantasy applications is that it can be used to assemble an integrated ranking of batters and pitchers for drafting purposes.

To calculate RAR for batters:

•    Start with a batter’s runs created per game (RC/G).

•    Subtract his position’s replacement level RC/G.

•    Multiply by number of games played: (AB - H + CS) / 25.5.

Replacement levels used in this book:

POS    AL      NL
C     3.18    3.28
1B    4.09    4.30
2B    3.45    3.40
3B    3.71    3.78
SS    3.41    3.18
LF    3.64    3.83
CF    3.78    3.66
RF    3.90    4.03
DH    4.28

RAR can also be used to calculate rough projected team won-loss records. (Roger Miller) Total the RAR levels for all the players on a team, divide by 10 and add to 53 wins.

Runs created  (Bill James) 

(H + BB – CS) x (Total bases + (.55 x SB)) / (AB + BB)

A formula that converts all offensive events into a total of runs scored. As calculated for individual teams, the result approximates a club’s actual run total with great accuracy.

Runs created per game  (RC/G)

Bill James version: Runs Created / ((AB - H + CS) / 25.5)

RC expressed on a per-game basis might be considered the hypothetical ERA compiled against a particular batter. Another way to look at it:  A batter with a RC/G of 7.00 would be expected to score 7 runs per game if he were cloned nine times and faced an average pitcher in every at bat. Cloning batters is not a practice we recommend. BENCHMARKS: Few players surpass the level of a 10.00 RC/G, but any level more than 7.50 can still be considered very good. At the bottom are levels less than 3.00.

Plate Appearances as a leading indicator  (Patrick Davitt) 

While targeting players “age 26 with experience” as potential breakout candidates has become a commonly accepted concept, a study has found that cumulative plate appearances, especially during the first two years of a young player’s career, can also have predictive value in assessing a coming spike in production. Three main conclusions:

•    When projecting players, MLB experience is more important than age.

•    Players who amass 800+ PAs in their first two seasons are highly likely to have double-digit value in Year 3.

•    Also target young players in the season where they attain 400 PAs, as they are twice as likely as other players to grow significantly in value.


1.    While pure southpaws account for about 27% of total ABs (RHers about 55% and switch-hitters about 18%), they hit 31% of the triples and take 30% of the walks.

2.    The average lefty posts a batting average about 10 points higher than the average RHer. The on base averages of pure LHers are nearly 20 points higher than RHers, but only 10 points higher than switch-hitters.

3.    LHers tend to have a better batting eye ratio than RHers, but about the same as switch-hitters.

4.    Pure righties and lefties have virtually identical power skills. Switch-hitters tend to have less power, on average.

5.    Switch-hitters tend to have the best speed, followed by LHers, and then RHers.

6.    On an overall production basis, LHers have an 8% advantage over RHers and a 14% edge over switch-hitters.

Skill-specific aging patterns for batters  (Ed DeCaria)

Baseball forecasters obsess over “peak age” of player performance because we must understand player ascent toward and decline from that peak to predict future value. Most published aging analyses are done using composite estimates of value such as OPS or linear weights. By contrast, fantasy GMs are typically more concerned with category-specific player value (HR, SB, AVG, etc.). We can better forecast what matters most by analyzing peak age of individual baseball skills rather than overall player value.

For batters, recognized peak age for overall batting value is a player’s late 20s. But individual skills do not peak uniformly at the same time:

Contact rate (ct%): Ascends modestly by about a half point of contact per year from age 22 to 26, then holds steady within a half point of peak until age 35, after which players lose a half point of contact per year.

Walk rate (bb%): Trends the opposite way with age compared to contact rate, as batters tend to peak at age 30 and largely remain there until they turn 38.

Stolen Base Opportunity (SBO): Typically, players maintain their SBO through age 27, but then reduce their attempts steadily in each remaining year of their careers.

Stolen base success rate (SB%): Aggressive runners (>14% SBO) tend to lose about 2 points per year as they age. However, less aggressive runners (<=14% SBO) actually improve their SB% by about 2 points per year until age 28, after which they reverse course and give back 1-2 pts every year as they age.

GB%/LD%/FB%: Both GB% and LD% peak at the start of a player’s career and then decline as many hitters seemingly learn to elevate the ball more. But at about age 30, hitter GB% ascends toward a second late-career peak while LD% continues to plummet and FB% continues to rise through age 38.

Hit rate (h%): Declines linearly with age. This is a natural result of a loss of speed and change in batted ball trajectory.

Isolated Power (ISO): Typically peaks from age 24-26. Similarly, home runs per fly ball, opposite field HR %, and Hard Hit % all peak by age 25 and decline somewhat linearly from that point on.

Catchers and late-career performance spikes  (Ed Spaulding) 

Many catchers—particularly second line catchers—have their best seasons late in their careers. Some possible reasons why:

1.    Catchers, like shortstops, often get to the big leagues for defensive reasons and not their offensive skills. These skills take longer to develop.

2.    The heavy emphasis on learning the catching/ defense/pitching side of the game detracts from their time to learn about, and practice, hitting.

3.    Injuries often curtail their ability to show offensive skills, though these injuries (typically jammed fingers, bruises on the arms, rib injuries from collisions) often don’t lead to time on the disabled list.

4.    The time spent behind the plate has to impact the ability to recognize, and eventually hit, all kinds of pitches.

Spring training Slg as leading indicator  (John Dewan)  

A hitter’s spring training Slg .200 or more above his lifetime Slg is a leading indicator for a better than normal season.

Overall batting breakout profile  (Brandon Kruse)  

We define a breakout performance as one where a player posts a Roto value of $20+ after having never posted a value of $10. These criteria are used to validate an apparent breakout in the current season but may also be used carefully to project a potential upcoming breakout:

•    Age 27 or younger

•    An increase in at least two of: h%, PX or Spd

•    Minimum league average PX or Spd (100)

•    Minimum contact rate of 75%

•    Minimum xBA of .270

In-Season Analysis

Batting order facts  (Ed DeCaria)  

Eighty-eight percent of today’s leadoff hitters bat leadoff again in their next game, 78% still bat leadoff 10 games later, and 68% still bat leadoff 50 games later. Despite this level of turnover after 50 games, leadoff hitters have the best chance of retaining their role over time. After leadoff, #3 and #4 hitters are the next most likely to retain their lineup slots.

On a season-to-season basis, leadoff hitters are again the most stable, with 69% of last year’s primary leadoff hitters retaining the #1 slot next year.

Plate appearances decline linearly by lineup slot. Leadoff batters receive 10-12% more PAs than when batting lower in the lineup. AL #9 batters and NL #8 batters get 9-10% fewer PAs. These results mirror play-by-play data showing a 15-20 PA drop by lineup slot over a full season.

Walk rate is largely unaffected by lineup slot in the AL.  Beware strong walk rates by NL #8 hitters, as much of this “skill” will disappear if ever moved from the #8 slot.

Batting order has no discernable effect on contact rate. 

Hit rate slopes gently upward as hitters are slotted deeper in the lineup. 

As expected, the #3-4-5 slots are ideal for non-HR RBIs, at the expense of #6 hitters. RBIs are worst for players in the #1-2 slots. Batting atop the order sharply increases the probability of scoring runs, especially in the NL. 

The leadoff slot easily has the highest stolen base attempt rate. #4-5-6 hitters attempt steals more often when batting out of those slots than they do batting elsewhere. The NL #8 hitter is a SB attempt sink hole. A change in batting order from #8 to #1 in the NL could nearly double a player’s SB output due to lineup slot alone.

DOMination and DISaster rates 

Week-to-week consistency is measured using a batter’s BPV compiled in each week. A player earns a DOMinant  week if his  BPV was greater or equal to 50 for that week. A player registers a DISaster if his BPV was less than 0 for that week. The percentage of Dominant weeks, DOM%, is simply calculated as the number of DOM weeks divided by the total number of weeks played.

Is week-to-week consistency a repeatable skill? (Bill Macey)

To test whether consistent performance is a repeatable skill for batters, we examined how closely related a player’s DOM% was from year to year. 

    YR1 DOM%    AVG YR2 DOM%
    < 35%          37%
    35%–45%        40%
    46%–55%        45%
    56%+           56%
Quality/consistency score (QC)

(DOM% – (2 x DIS%)) x 2) 

Using the DOM/DIS percentages, this score measures both the quality of performance as well as week–to-week consistency. 

Sample size reliability  (Russell Carleton)  

At what point during the season do stats become reliable indicators of skill? Measured in PA (unlisted=did not stablize over full season):

100:    Contact rate

150:    Strikeout rate, line drive rate, pitches/PA

200:    Walk rate, ground ball rate, GB/FB

250:     Fly ball rate

300:     HR rate, hr/f

500:     OBP, Slg, OPS

550:     Isolated power

Projecting RBIs  (Patrick Davitt)  

Evaluating players in-season for RBI potential is a function of the interplay among four factors:

•    Teammates’ ability to reach base ahead of him and to run the bases efficiently

•     His own ability to drive them in by hitting, especially XBH

•     Number of Games Played

•     Place in the batting order

    3-4-5 Hitters: 

(0.69 x GP x TOB) + (0.30 x ITB) + (0.275 x HR) – (.191 x GP)

    6-7-8 Hitters:

(0.63 x GP x TOB) + (0.27 x ITB) + (0.250 x HR) – (.191 x GP)

    9-1-2 Hitters: 

(0.57 x GP x TOB) + (0.24 x ITB) + (0.225 x HR) – (.191 x GP)

    ...where GP = games played, TOB = team on-base pct. and ITB = individual total bases (ITB)

Apply this pRBI formula after 70 games played or so (to reduce the variation from small sample size) to find players more than 9 RBIs over or under their projected RBI. There could be a correction coming.

You should also consider other factors, like injury or trade (involving the player or a top-of-the-order speedster) or team SB philosophy and success rate.

Remember: the player himself has an impact on his TOB. When we first did this study, we excluded the player from his TOB and got better results. The formula overestimates projected RBI for players with high OBP who skew his teams’ OBP but can’t benefit in RBI from that effect.

Ten-Game hitting streaks as a leading indicator  (Bob Berger)

Research of hitting streaks from 2011 and 2012 showed that a 10-game  streak can reliably predict improved longer-term BA performance during the season. A player who has put together a hitting streak of at least 10 games will improve his BA for the remainder of the season about 60% of the time. This improvement can be significant, on average as much as .020 of BA. 

Other Diamonds

It’s a Busy World Shortcut

For marginal utility-type players, scan their PX and Spd history to see if there’s anything to mine for. If you see triple digits anywhere, stop and look further. If not, move on.

Chronology of the Classic Free-Swinger with Pop

1. Gets off to a good start.

2. Thinks he’s in a groove.

3. Gets lax, careless.

4. Pitchers begin to catch on.

5. Fades down the stretch.

Errant Gust of Wind

A unit of measure used to describe the difference between your home run projection and mine.

Hannahan Concession

Players with a .218 BA rarely get 500 plate appearances, but when they do, it’s usually once.

Mendoza Line

Named for Mario Mendoza, it represents the benchmark for batting futility. Usually refers to a .200 batting average, but can also be used for low levels of other statistical categories. Note that Mendoza’s lifetime batting average was actually a much more robust .215.

Old Player Skills

Power, low batting average, no speed and usually good plate patience. Young players, often those with a larger frame, who possess these “old player skills” tend to decline faster than normal, often in their early 30s.

Small Sample Certitude

If players’ careers were judged based what they did in a single game performance, then Tuffy Rhodes and Mark Whiten would be in the Hall of Fame.

Esix Snead List

Players with excellent speed and sub-.300 on base averages who get a lot of practice running down the line to first base, and then back to the dugout. Also used as an adjective, as in “Esix-Sneadian.”