Top Five Greatest Hitters with Under 200 Home Runs


This past week we saw two monumental events in the world of baseball. One was the passing of Hall of Famer and long time Mets broadcaster, Ralph Kiner. The other was the announcement from Derek Jeter that 2014 will be his final season, a sure fire first ballot Hall of Famer (hopefully). What’s special about these two men was that they have been able to have Hall of Fame careers, without amassing a large amount of home runs. Ralph Kiner retired with 369 career home runs, albeit he would’ve had more if injuries did not cut his career short. While Derek Jeter will enter his final season with 256 career home runs. Their combined home run total of 625 puts them behind Barry Bonds, Hank Aaron, Babe Ruth, Willie Mays, Alex Rodriguez and Ken Griffey Jr.’s all time mark, just to give an idea of how far behind they are to the great power hitters of our game. Like Kiner and Jeter, there have been plenty of great hitters who have made a name for themselves without breaking any significant home run milestones. So, this post will pay homage to the greatest hitters of the live ball era (after the 1919 season) who have not hit 200 career home runs.

5. Tony Gwynn (1982-2001).338/135/1138/.847

If there ever was a professional hitter, it would be Tony Gwynn. His stroke was perfect and he always put the ball in play. He never struck out more than 40 times in a season, which was impressive then and is even more so now as league strikeout rates are at record highs. On top of that, he led the league in hitting eight out of his 20 years in the big leagues. However, his lack of run production hurts him in the rankings and his Offensive Wins Above Replacement (oWAR) backs this claim up. Out of the five, he has the lowest career oWAR at 66.3. While he was one of the best at getting base hits, it wasn’t enough to make up for his lack of runs driven in compared to the rest of the list.

4. Paul Waner (1926-1945) – .333/113/1309/.878

In 1927, Paul Waner had one of the most unusual statistical seasons for a hitter. While only hitting 9 HRs, he led the league with 131 RBIs and 342 Total Bases. On top of that he led the league in batting average, hits, triples, plate appearances and games played on his way to winning the league MVP and helping the Pirates win the 1927 National League pennant. Unfortunately for us, batting average with runners in scoring position was not kept in 1927. One can only imagine how high it must have been to have an RBI total that high, with so few long balls. Outside of his impressive 1927 campaign, Waner was a consistent hitter for the Pirates through his late 30s. He led the league in hitting three times and had nine seasons with 75 or more RBIs.

3. Charlie Gehringer (1924-1942) – .320/184/1427/.884

Charlie Gehringer had one of my favorite nicknames of all time, the Mechanical Man. He earned this nickname due to his stoic nature and his consistency on the field. A no frills player who earned the respect of his much hated, yet talented manager, Ty Cobb. Gehringer’s stat lines are more reminiscent of Cobb’s era than the 1920s and 1930s, during his prime years. He had seven seasons where he had over 100 RBIs, yet never hitting more than 20 HRs. While he only led the league in hitting once, he had a span where he finished in the top five for five consecutive years from 1933-1937. Always consistent.

2. Pete Rose (1963-1986) – .303/160/1314/.784

The all-time hits, games played, plate appearances and at-bats leader and due to a Bud Selig power trip, the only non-Hall of Famer on the list. Regardless, Rose’s career was nothing short or brilliant. In his long career, he had ten 200 hit seasons, ten 100 run seasons on his way to breaking Ty Cobb’s all-time hit record with 4,256 career hits. Similar to Gwynn, his run production numbers hindered his ranking, although not as severely as Gwynn. There’s a little more leeway when you are the all-time hit king. Finally, put Pete Rose in the Hall of Fame!

1. Wade Boggs (1982-1999) – .328/118/1014/.858

This is probably the most controversial part of these rankings. In terms of career oWAR, Boggs leads everyone on the list at 80.5, although Rose’s career oWAR is 82.5, he played in 6 more seasons than Boggs. Why is this so? He got on base more. This reasoning is starting to sound like a sabermetrician’s cop out argument to balk at any debates over the validity of advanced metrics, but there is legitimacy to it. Wade Boggs has a career OBP of .415, which pure him at 26th all-time. That is also forty points higher than Pete Rose’s career OBP of .375. Also, we must look at the statistic, career Runs Created per Game (RC/G). This stat measures the offensive value of the player by weighing all his offensive events based on importance, run scoring opportunities, power and on base ability, among other things, and is vital in calculating oWAR. When we figure out the RC/G for their career we can see who was more valuable and productive with their hitting. Our calculations puts Wade Boggs ahead of Pete Rose with a career RC/G of 7.1, compared to Rose’s 5.8. While this may be surprising to some this is a great example of what advanced metrics does exactly. Find hidden value. So, while Rose has higher career numbers, Boggs was the more valuable and productive hitter. But who knows, maybe in Boggs’ WAR calculation they factored in his legendary drinking ability to give him a boost in the rankings.

The History of WAR

Mike Trout, the true most valuable player in the last two years, according to Wins Above Replacement

Mike Trout, the true most valuable player in the last two years, according to Wins Above Replacement

WAR.  Wins Above Replacement.  Never has a statistic been used so much, yet understood so little.  Almost every baseball writer, including myself, has avoided writing about it directly.  Rather, we use it as a reference and assume the reader has the same vague understanding of it as we do.  To be fair, discussing WAR has been avoided for two legit reasons.  One it is complicated to understand, and two, explaining it would absolutely kill any flow of an article or blog post due to its complexity.  So, without boring you all to tears, we’ll conceptualize WAR in the historical sense to understand how it came about.


In the beginning there was darkness, then Bill James said let there be light; and there was light.  And James saw the light and it was good.  Maybe that was a little over the top, but there’s a point to it.  Almost everything in baseball analytics originates from Bill James and WAR is no exception.  Before there was WAR, there was Runs Created (RC).  Runs Created was one of the first advanced statistics, created by James in 1979.  It was the first attempt to quantify a player’s holistic contribution to his team.  The original 1979 equation for Runs Created is the following:

(Hits + Walks)/(At Bats + Walks)*(Total Bases/At Bats)*At Bats

Essentially, this equation multiples On Base Percentage by Slugging Percentage by At Bats.  It estimates the number of runs a player contributes to the team.  Since it’s inception, the equation has been tweaked and has added on other variables to make it much more complicated.  Perhaps the most revolutionary thing about RC was that it rejected the traditional way of looking at baseball statistics.  In Bill James’ groundbreaking book, Baseball Abstract, he uses RC as the go to stat to demonstrate player value.  Player value differs from the traditional statistics such as HRs or RBIs in that it measures a player’s worth to the team, rather than tally an individual’s performance.  After all, general managers don’t pay for batting averages, they pay for wins.

Sharing is Caring and Confusing..and Useless

Throughout the 1980s and 1990s, Runs Created was the statistic for player value and nothing much changed except for the addition of extra variables to the original equation.  That all changed in 2002.  If you notice, Runs Created does not address the value of defense.  Although the 1979 equation does not state it, by 2002 the RC equitation takes into account base running.  After what James said was years of research he finally believed he found how to measure defensive value and with it, total player value.  In perhaps his most famous and controversial theory, James lays out his theory of Win Shares in two books he released in 2002, Win Shares and The New Historical Baseball Abstract.

Win Shares is where things get confusing.  Very confusing.  To generate a position player’s Win Shares is an arduous task.  It involves concepts such as Marginal Runs, Marginal Runs Saved, Fielding Independent Pitching, Team Defensive Efficiency Rating and other statistics James created to support his Win Shares theory.

While this was the first comprehensive statistic to evaluate a player’s offensive and defensive abilities, there were a few problems with it.  One was the calculations.  The many, many calculations.  To demonstrate the length and level of difficulty to find a player’s Win Shares, I did the equation myself for a few players.  The average time to produce a player’s Win Shares was 23 minutes.  Not very efficient.  Also, these series equations are layered with many steps that would even take computers a little time to figure out because of the immense amount of player and league average data you need to solve for one player.  Another problem with Win Shares is that it emphasized a player’s contribution to their current team, instead of contribution to a team.  This is probably the biggest critique to Win Shares.  It favors great players who played for great teams.  For example, the greats of the 1950’s New York Yankees teams have inflated Win Share totals.  While Mickey Mantle had a legendary but injury laden career, he ranks as the 10th greatest player of all-time, according to Win Share totals.  This is not right.  Due to his injuries, Mantle’s power, defensive and base running abilities were limited and hindered his production.  Statistically, Willie Mays had a better career and better peak seasons, yet he ranks below Mantle on the all-time list.  There has to be a better way….

WAR and Peace

Once James published his controversial theory on Win Shares, there was a backlash from not only the baseball traditionalist community, but also the SABR community.  The geniuses at started to get to work at correcting the biggest flaw in the Win Shares theory, comparing the player relative to his team.  They sought out to find a way to measure a player versus the league average, Player X.  This stat would be called Wins Above Replacement, WAR.

One of the key elements of their calculations is in the world of WAR, everything is weighted.  Weighted statistics are an easier way to analyze a player’s performance versus the league average.  Instead of a tally of a certain statistic, it assigns a number value to a player’s statistic versus the league average.  An example of this is OPS+.  Furthermore, weighted statistics are vital on how to calculate a player’s WAR, because WAR compares a player to league average, like weighted statistics.  Weighted and adjusted statistics also make it easier to group a bunch of statistics into one super statistic, such as weighted Runs Against Average (wRAA).  This saves time and energy when comparing a player’s performance against the league.

What does WAR take into account when measuring player value?  Everything.  Even things you would not think of, such as the number of extra base hits with two runners on and one outs.  Also, all of these events have a linear weight to determine it’s impact on the game.  For example, an outfield single carries more weight than an infield single, because an outfield single has a greater probability of advancing runners and scoring runs than an infield single.  Every event has a value and these events are grouped into various statistics such as Baserunning Runs (Rbr) and defensive Fielding Runs for a player’s defensive performance.

Who is Player X?

Determining the value of Player X, the replacement level to be compared against all players, is the most controversial and least understood aspect of determining Wins Above Replacement due to its subjectivity.  First off, the value for Player X changes every year to match the level of competition in the given year.  The replacement level is found by determining the average of the aforementioned weighted statistics, especially wRAA.  Once all the averages are found and equated, we have replacement level WAR.

End Game

Once the replacement level WAR is found, we can begin to find an individual player’s WAR.  We do this simply by entering in the player’s information into the same equation we did to find the replacement level WAR.  When this is completed we are left with Wins Above Average (WAA).  Finally, we add the replacement level WAR to a player’s WAA.  We then have found a player’s Wins Above Replacement.  So, in very, very short, the equation to determine a player’s WAR is:

Replacement Level WAR + WAA = WAR


You probably thought we were done there.  Nope!  The standard barer for WAR calculations has been  However, other notable baseball think tanks have their own WAR theories.  Fan Graphs has fWAR (Fan Graphs Wins Above Replacement) and Baseball Prospectus has Wins Above Replacement Player (WARP).  Both WARP and fWAR are different from each other and WAR.  All three use different statistics and methods when determining a player’s value.  It would take forever and a day to go over all the differences, so here is a neat graph that goes over the similarities and differences.  Also, all three of these equations are constantly tweaked and updated when new information becomes available, or when a new stat or method is discovered.  The most recent update being in March 2013.


The history of determining player value is short, but very complex.  While WAR right now stands as baseball’s God equation, there will surely be more ways to find the true value of players in the future as the sports becomes even more in twined with technology.  For now, WAR is the best thing we’ve got to answer baseball’s biggest question of, who is the most valuable player in the league?

Two Joes and a Show Podcast: Super Bowl Wrap-Up, Peyton Manning’s Next Move and our Super Bowl 49 Picks


Our post-Super Bowl podcast is ready! We go over what happened to Denver, the amazing Seattle game plan and how this game impacts the legacy of Peyton Manning. We also make our “picks” for Super Bowl 49! All that and more in this week’s episode! Please download, subscribe and rate!

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As for my readers.  I apologize for not being able to post this week.  The Super Bowl and the extensive research for my next post has taken up more time than I originally planned.  My next post will be up on Saturday, I’ll try not to disappoint!