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My brother recently Tweeted that it would be a “crime” if Mets pitcher Jacob deGrom didn’t win the National League Cy Young award. But is that the case? We’ll take a look. In my opinion, the three main candidates for the NL Cy Young this year are deGrom, Max Scherzer of the Washington Nationals, and Aaron Nola of the Philadelphia Phillies. Honorable mentions go to Clayton Kershaw of the Los Angeles Dodgers, Miles Mikolas of the St. Louis Cardinals, and Patrick Corbin of the Arizona Diamondbacks. Take a look at the chart below to see how each of these 6 players compare in baseball’s main pitching metrics: To start with determining my Cy Young winner, I will rank each pitcher by adding up his total ranking for each statistic compared to the other 5 pitchers. To give credit for finishing in the top 10 of a category, the number of top 10s is subtracted from the ranking totals. That way players that finish 6th among the top 6 pitchers and 6th in the league overall finish better than players who finish 6th among the top 6 pitchers but 30th in the league overall. See the table below for the carrying out of this process: This way finds that Scherzer, not deGrom, should be this year’s NL Cy Young winner. However, it only uses 6 pitching statistics and assumes that they are all valued equally. Taking more stats into consideration, we find that Scherzer also comes first in Walks & Hits per IP, Strikeouts per 9 IP, Situational Wins, Shutouts (tied), and Strikeouts / Base on Balls. On the flip side, deGrom also leads the NL in ERA+, Adjusted Pitching Wins, Base-Out Runs Saved, Base-Out Wins Saved, Adjusted Pitching Runs, Fielding Independent Pitching, and Win Probability Added (WPA). You, and me as well, don’t know what most of these stats even measure or mean, but the main idea is that deGrom leads the NL in many complex metrics that I didn’t take into consideration. In the most traditional sense, Scherzer is the leader of wins and strikeouts, while deGrom is the leader of ERA. However, deGrom leads the league in ERA by far. His 1.70 is the lowest ERA in the NL since Zack Greinke’s 1.66 in 2015. Though Greinke didn’t win the Cy Young that year, it is important to note that the winner that year – Jake Arrieta – was very close behind at 1.77. The next time 1.70 or better was done was Greg Maddux’s 1.63 in 1995. He did win the Cy Young that year, largely because the next lowest ERA was 2.54 by Hideo Nomo. To sum it all up, if your ERA is absurdly lower than the rest of the competition (we’ll define “absurdly” as around .50 or more), then you deserve, and often times do end up receiving, the Cy Young award. Let’s look at how this phenomenon has worked over time: As the table above shows, of the 21 times that a league’s ERA leader was at least .47 lower than the rest of the pitchers, that pitcher won the Cy Young 14 times. However, since the Cy Young wasn’t given to both leagues until 1967, we can ignore the instance in 1962 since an NL pitcher won the award. We can assume Whitey Ford would have won the AL Cy Young award that year had there been such a thing. Therefore, 14 out of 20 times, or 70% of the time, a pitcher with an ERA .47 or lower than the rest of the league wins the Cy Young. In years where this is not true, the winning pitcher generally had significantly more wins and strikeouts than his low ERA adversary. The 1990 and 2003 awards are exceptions to this – I believe the voters got the award wrong for those years. Since deGrom’s ERA of 1.70 is .67 than the rest of the league, we can say that there’s roughly a 70% chance he takes home the Cy Young this year. Since his difference is higher than most and his ERA is lower than most as well, we can expect this percentage to be higher as well. Scherzer’s 300 strikeouts on the season are certainly impressive – but just last year Chris Sale also reached that mark and came up short to the ERA leader Corey Kluber. To put it all to rest, I’ll state it here: Jacob deGrom deserves and should be this year’s NL Cy Young winner. However, a victory for Scherzer is not totally out of the question as the win – strikeout combo has been favored before despite the fact that a pitcher has had such a lower ERA than everyone else. Thank you for reading as always, Aaron Springer Sources: https://en.wikipedia.org/wiki/List_of_Major_League_Baseball_annual_ERA_leaders https://en.wikipedia.org/wiki/Cy_Young_Award#National_League_(1967%E2%80%93present) https://en.wikipedia.org/wiki/List_of_Major_League_Baseball_annual_strikeout_leaders https://www.baseball-reference.com/players/d/degroja01.shtml https://www.baseball-reference.com/players/s/scherma01.shtml https://www.baseball-reference.com/players/n/nolaaa01.shtml https://www.baseball-reference.com/leagues/NL/2018-pitching-leaders.shtml https://www.baseball-reference.com/players/k/kershcl01.shtml https://www.baseball-reference.com/players/c/corbipa01.shtml https://www.baseball-reference.com/players/m/mikolmi01.shtml https://www.baseball-reference.com/players/c/clemero02.shtml https://www.baseball-reference.com/players/b/brownke01.shtml https://www.baseball-reference.com/players/r/ryanno01.shtml https://www.baseball-reference.com/players/m/martipe02.shtml https://www.baseball-reference.com/players/a/aguirha01.shtml https://www.baseball-reference.com/players/f/fordwh01.shtml
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We use all types of data to help us understand which players are worth more than others overall and in certain categories, but many people may not know what these metrics mean and/or how they are calculated. Because of this, I’ve decided to help define and explain some of baseball’s most popular statistical metrics. I will be assuming readers have at least somewhat of a baseball knowledge and thus will skip the most obvious stats such as plate appearances, at-bats, hits, home runs, runs batted in, runs, strikeouts, walks, hit by pitches, singles, doubles, triples, stolen bases, wins, losses, saves, etc. Batting Average (BA) : A player’s batting average is used to describe about how often a player records a base hit. It is found by dividing a player’s hits by his at-bats (BA = H / AB). For example, if I were to get 3 hits in a span of 10 at-bats, I would have a batting average of .300 On Base Percentage (OBP) : On base percentage is used to describe about how often a player gets on base. It is notably different from the batting average in that it takes walks (BB), hit by pitches (HBP), and sacrifice flies (SF) into consideration. Note that sacrifice bunts are not included since they are usually managerial calls with the intention of getting the batter out and that errors are not included, even though the runners ends up on base, because the metric is meant to measure a batter’s ability to get on base, not how “lucky” he may be from fielders making errors. Thus OBP is found by adding up the hits, walks, and hit by pitches of the batter and dividing it by the number of at-bats, walk, hit by pitches, and sacrifice flies. (OBP = (H + BB + HBP) / (AB + BB + HBP + SF)). For example, if I were to get 3 hits in 10 at-bats, get walked twice, hit by a pitch once, and sacrifice fly once, my OBP would be (3 + 2 + 1) / (10 + 2 + 1 + 1) = 6 / 14 = .429 Total Bases (TB) : Most teams would prefer a player hit 200 doubles over 200 singles, 200 triples over both, and 200 home runs over all else. It is always easier to score a runner from third than from first, or better yet have him score himself. Since more bases is obviously preferable, it is important to distinguish the total number of bases a player gets from the total number of hits he gets. Think of total bases as like “weighted” hits. Total bases is found by simply multiplying each type of hit by the number of bases it gets a player (TB = 1 x 1B + 2 x 2B + 3 x 3B + 4 x HR). For example, if I hit 3 singles, 2 doubles, a triple, and 3 home runs, I would have 9 hits (3 + 2 + 1 + 3) but 22 total bases (1 x 3 + 2 x 2 + 3 x 1 + 4 x 3 = 3 + 4 +3 + 12). Comparatively, if I only hit 9 singles, I would still have 9 hits but also only 9 total bases. In short, total bases is a more detailed, specific, and relevant statistic than hits. Maybe I’ll take a look at this later ;) Slugging Percentage (SLG) : Slugging percentage can be seen as the older brother of batting average. Instead of measuring a batter’s efficiency with his number of hits, it uses his total bases instead. Thus, slugging percentage is found by dividing the total bases from a player’s at-bats (SLG = TB / AB). For example, if I were to bat 1.000 from the above example with 9 singles, my batting average would obviously be 1.000 and so would my slugging percentage. However, if use the other example above and batted 1.000, my slugging percentage would be much higher at 2.444 (22 / 9). On Base Plus Slugging (OPS) : This one is simply what it’s named; found by adding a player’s OBP with his SLG. You can also do this with a player’s total base, at-bats, walks, hit by pitches, and hit statistics, but that results in a longer unnecessary equation you shouldn’t worry about. OPS doesn’t mean anything specifically, but it’s a good way to measure a player’s contributions on offense and a team can know that in general a player with a higher OPS is more preferable over one with a lower OPS. (OPS = OBP + SLG). For example, if my OBP is .400 and my SLG is .500, my OPS would be .900 Park Factor (PF) : Now we start getting into some more complicated matters. We all know that some parks are just simply easier to get hits, and home runs, than others. This is largely due to differing altitudes and fence lengths. Park factor is found by dividing a certain teams runs scored and allowed per game, for home games, by that same teams run scored and allowed per game, for road games, and then multiplying by 100. (PF = ((home runs scored + home runs allowed) / home games) / ((away runs scored + away runs allowed) / away games) x 100). For example, if my team scored 10 runs at home and allowed 8 runs at home in 4 home games, and scored 5 runs on the road and allowed 4 runs on the road in also 4 road games, my PF would be ((10 + 8) / 4) ((5 + 4) / 4) x 100 = (18 / 4) / (9 / 4) x 100 = (4.5 / 2.25) x 100 = 2 x 100 = 200. Any PF over 100 means that particular team’s ballpark is “batter friendly”; on the reverse side, a PF below 100 means that team has a “pitcher friendly” ballpark. PF is truly a good indicator of which ballparks allow for more/less runs scored since both teams’ runs are taken into consideration throughout the season. Since my PF was 200, this means my ballpark is very batter friendly and more specifically that teams are twice as likely to score runs at my park than others. Park Factors can also be expressed by not multiplying by 100 (mine would then be 2); also realize that 2 is a very high PF and most are around 1. Note that the PF of a park can change with each season and that you could find the average PF of a park throughout its lifespan. On Base Plus Slugging Plus (OPS+) : Since players that play at parks with higher PFs are likely to have a higher OPS, OPS+ tries to normalize a player’s OPS with everyone else in the MLB by taking the park factor out of the equation. It is found by dividing a player’s OBP with the league’s average OBP, adding that with a player’s SLG divided by the league’s average SLG, subtracting by 1, dividing that by the player’s team’s PF, and then multiply again by 100. The idea is that the league average OPS will have an OPS+ of 100, so that OPS+ above or below 100 show how good a player is doing compared to most players in the league. (OPS+ = (((OBP/lgOBP + SLG/lgSLG) - 1) / PF ) x 100). For example, if my OBP was .3, the league average OBP was .4, my SLG was .4, the league average SLG was .5, and my PF was 2, my OPS+ would be (((.3/.4 + .4/.5) – 1) / 2) x 100 = (((.75 + .8) – 1) / 2) x 100 = ((1.55 – 1) / 2) x 100 = (.55 / 2) x 100 = 27.5. Since 100 is the league average OPS+, and I’m only at 27.5, you can see that I my OPS+ is well below average and my high PF plays a big factor in that. Fielding Percentage (Fld%) : This simply measures how reliable of a fielder a player is. It is found by dividing the total of a player’s putouts (PO) and assists (A) with his defensive chances (DC). Defensive chances are simply the sum of a player’s putouts, assists, and errors (E). Assists are simply any time a player touches the ball before a putout is recorded, and a putout is simply whenever a player actually gets someone out. If I’m playing shortstop and throw somebody out at first, I would get an assist and the first baseman would get a putout. (Fld% = (A + PO ) / (A + PO + E)). For example, if I record 5 assists, 5 putouts, and 3 errors, my fielding percentage would be (5+5) / (5 + 5 + 3) = 10 / 13 = .769 Range Factor Per Game (RF/G) : Pretty much just how many defensive plays a player makes in each game he plays. Found by dividing the sum of a player’s putouts and assists by his games played. (RF/G = (PO + A) / G). For example, if I get 5 putouts and 3 assists in 2 games, my RF/G would be (5 + 3) / 2 = 8 / 2 = 4 Range Factor Per 9 Innings (RF/9) : Since playing in a baseball game could mean only playing in one inning or even one at-bat, range factor per game isn’t as accurate as it could be. RF/9 normalizes a player’s total innings as if all the games he had played were full, complete games. It if found by multiplying the sum of a player’s putouts and assists by 9, and then dividing that by the number of games he played. (RF/9 = (9 x (PO + A)) / Innings). For example, if we use the same numbers from the previous example, and I record 5 putouts and 3 assists in 12 innings (technically 2 games), then my RF/9 would be ((5 + 3) x 9) / 12 = (8 x 9) / 12 = 72 / 12 = 6. This is a higher number compared to the one found with RF/G, as it would have taken the same number of plays in 18 innings to match that. Earned Run Average (ERA) : This is used to find about how many runs a pitcher would allow, on average, if he were to pitch a complete game. It is found by multiplying his earned runs (runs scored not from fielder errors) by 9 and dividing it by the number of innings pitched. (ERA = (9 x ER) / IP). For example, if I gave up 5 runs in 9 innings, my ERA would be 5; however, if I gave up 5 runs in just 4 innings, my ERA would be (9 x 5) / 4 = 45 / 4 = 11.25 Adjusted Earned Run Average (ERA+) : Similar to OPS+, this is the ERA but with park factors taken out of consideration as if every pitcher were able to pitch in the same park, without any ballpark advantages or disadvantages. It is found by dividing a pitcher’s ERA from the league average ERA, multiplying that by that pitcher’s team’s PF, and then multiplying that by 100. (ERA+ = (lgERA / ERA) x PF x 100). For example, if my ERA is 3, the league average ERA is 3.10, and my PF is 1.2, my ERA+ would be (3.1/3) x 1.2 x 100 = 1.033 x 120 = 123.96à124. Thus my ERA is about 24% better than the league average. That concludes the first edition of Defining Statistics. Hopefully you now know more about these 12 metrics and how each of them can be used to compare the productivity of players. Be on the lookout for the next edition of defining statistics to learn about the common metrics used in baseball (WHIP, RISP, WAR, who knows what I’ll cover next). Thank as always, Aaron Springer Sources used: https://en.wikipedia.org/wiki/On-base_percentage https://en.wikipedia.org/wiki/Slugging_percentage https://en.wikipedia.org/wiki/On-base_plus_slugging http://m.mlb.com/glossary/advanced-stats/on-base-plus-slugging-plus https://www.baseball-reference.com/players/a/aaronha01.shtml https://en.wikipedia.org/wiki/Batting_park_factor https://en.wikipedia.org/wiki/Adjusted_ERA%2B https://www.baseball-reference.com/about/bat_glossary.shtml
Recently, Yankees slugger Giancarlo Stanton belted his 300thcareer home run on August 31st. At age 28 and in just 1,119 games, he was the 5thfastest player to do so; beating him to it were Ralph Kiner, Ryan Howard, Juan Gonzalez, and Alex Rodriguez. As you may have noticed, none of the top three home run hitters in baseball history were quicker than Stanton. Therefore, we can conclude that Giancarlo will be our next home-run king, correct? Not so fast. Remember when Miguel Cabrera hit his 400thhome run back in 2015? He too was on a better pace than baseball’s home-run greats of Ruth, Aaron, and Bonds. However, in the past two seasons, Miggy has hit a depressingly low 19 home runs combined, making a home-run-record breaking career pretty much impossible. The truth of the matter with baseball’s home run record, and most of its records in general, is that it doesn’t matter how quickly or early a player accomplishes something. All that matters is that the player gets it done eventually and continues to do it consistently throughout the remainder of his career. Of the players that reached 300 quicker than Stanton, only one of them (Alex Rodriguez, 696 career home runs) went on to live up to -or at least around- his pace. Ralph Kiner only made it to 512. Juan Gonzalez only to 434. Andruw Jones, another one of the fastest players to reach 300, matched Gonzalez at 434. Ryan Howard failed to even continue to the 400-home run club, ending up with 382. The mid-to-late 30s (and at times even the early 30s) are a crucial period in a player’s career in terms of record-breaking. Players either hang around and continue to prove their worth or show their age and retire. It’s a large facet of what makes a player an all-time great. Let’s take a look at how several different players compare in their mid-to-late 30s: You probably see the pattern by now. The key to having a career with more home runs involves playing when you’re older and still hitting a lot of homers while doing so. With the exception of Ruth, all of the top career home run hitters (we’ll look at Pujols later) were hitting 25+ homers at age 39, whereas their counterparts that had more productive first-half careers were sitting at home on the couch at the same age. Breaking records is exciting. We all hope and prematurely predict players to break records, but unfortunately many of our predictions come up short. Players aren’t robots and many hit a sort of career wall by age 35. Stanton is on pace to be the next home run great, but he’ll have to keep it up for many years to come to be legitimate. The second half of a player’s career is just as important, if not more important, than the first half in deciding their relative greatness. With all that being said, the remainder of this week’s post will involve taking a look at today’s players and seeing who might (or might not) be giving the home run record a run for its money. I only used players that I found to be projected into the 600s, as well as notable active career home run leaders. Some important things to acknowledge before looking at the list are:
Before we start the actual list, let’s quickly find the number of additional home runs players will hypothetically hit the rest of this season as mentioned above: Now, we can finally proceed to our actual list: *note that the “current” home run values INCLUDE tthe Hypothetical Additional Home Runs calculated above* Please note below how I determined the average home runs per season for each player:
Thanks again for reading. I know this post was harder to understand and took more calculation time on my part, but hopefully you still found it interesting. Aaron Springer Sources Used: https://www.si.com/mlb/2015/05/16/miguel-cabrera-tigers-400th-career-home-run https://www.baseball-reference.com/bullpen/200_Home_Run_Club https://www.baseball-reference.com/bullpen/300_Home_Run_Club https://www.baseball-reference.com/bullpen/400_Home_Run_Club https://www.baseball-reference.com/bullpen/500_Home_Run_Club http://www.espn.com/mlb/story/_/id/24527546/yankees-slugger-giancarlo-stanton-hits-300th-hr-5th-fastest-mark https://www.baseball-reference.com/leaders/HR_active.shtml I also used the Baseball-Reference Pages for the following players: Barry Bonds Hank Aaron Babe Ruth Alex Rodriguez Willie Mays Juan Gonzalez Andruw Jones Ryan Howard Albert Pujols Adrian Beltre Miguel Cabrera Giancarlo Stanton Mike Trout Khris Davis Nolan Arenado Joey Gallo Aaron Judge Cody Bellinger
When Babe's in a Bond, a Hammer Comes to the Rescue: Deciding Who Should be Baseball's Home Run King9/9/2018 Before I start my second post I encourage you to take a look at my first post, which investigated the implications that the 1961 Major League Baseball increase in games played had on the total career hit count of Ty Cobb. We concluded (or better yet, I concluded, seeing that every response to last week’s poll still voted Pete Rose as baseball’s hit king) that the additional 8 games per year actually had a massive impact on Cobb’s statistical potential, as he was deprived of roughly 650 at-bats which was translated to about 225 more hits for his career. This week’s post will deal with a very similar issue, but instead of the impact that the switch to 162 games had on certain players’ career hits, we will look at the impact that the switch had on certain player’s career home runs. The subject of home run king has been an icy topic, as baseball’s current leader Barry Bonds (762 career homers) has been highly believed to have used steroids/PEDs. This controversy has thwarted Bonds from being inducted into Cooperstown despite more than enough achievements to deem him worthy of the reward. In fact, because of his alleged steroid use, many true baseball fans still consider Hank Aaron (755 home runs) to be the home run king. However, it is neither of these two leaders, but the third-place finisher Babe Ruth (714 home runs), who we will be taking the deepest look at. As I’ve mentioned before, in 1961 the MLB increased the number of games played in a season to 162 – 8 more than the previous 154 used since 1904 (with the notable exception of 1919, when 140 games were played). Bonds had the 162-game luxury his entire career, as he played from 1986-2002. Ruth, on the other hand, was deprived of these extra games every year of his career from 1914-1935. Unlike last week, this time around we will have an exciting third player and element to take into the picture, as Hank Aaron was actually also deprived of the 162-game mark for several seasons being that he played from 1954-1976. So again, let’s try to answer the question: If all three players were able to play 162 games each season, who would be the home run king? As I said last week, not every player plays in every single game of their team’s season. Thus, we will take the percentage of the 154 games that Ruth and Aaron played and use it to find the number of games they would have played in a 162-game season. Starting with “The Bambino”: ** We did not include the 1914 season since Ruth hit no home runs that year. He was primarily a pitcher during his stint with the Red Sox and thus deprived of more could-have-been homers. More on this later.** By adding up all of the New Games Played for each season, we get a New Career Games Played of 2,650 for Babe Ruth; 147 more games than his actual career total of 2,503 thanks to the MLB rule change. Now we’ll calculate the same thing for the 7 seasons in which “Hammerin’ Hank” was only able to play a maximum of 154 games: By adding up all of the New Games Played for each season with all of his career games played from 1961 to 1976, we get a New Career Games Played of 3,353 for Hank Aaron; 55 more games than his actual career total of 3,298 thanks to the MLB rule change. Now that we know how many extra games each player would have had, we can use this information to calculate how many extra at-bats they would have gotten as well. Again, we’ll start with the “Sultan of Swat”: Adding up the total of the New Total At Bats in Season gives us a New Total At Bats in Career of 8,890 for Babe Ruth; an extra 491 at bats more than his actual career total of 8,399 thanks to the MLB rule change. Again, now we’ll do the same process for Hank Aaron: Adding up the total of the New Total At Bats in Season with his total at bats from 1961-1976 gives us a New Total At Bats in Career of 12,577 for Hank Aaron; an extra 213 at bats more than his actual career total of 12,364 thanks to the MLB rule change. Lastly, we can now use each player’s New Total At Bats per Season and multiply it by their home-run percentage (home runs hit in season divided by at bats in season) in order to get their New Home Runs in Season. Then we’ll simply add up the new home runs in each season in order to get the new total career home runs for each player. Like before, we will start with the “Colossus of Clout”: For one last time, by adding up the total of the New Home Runs in Season, we get the New Total Home Runs in Career of 746 for Babe Ruth; 32 more home runs than his actual career total of 714 thanks to the MLB rule change. These numbers obviously show that Ruth would have been able to increase his home run amount by a fair amount, but still wouldn’t be have enough to top Aaron or Bonds. But will Aaron have the same fate? Let’s check it out: Adding up the New Home Runs in Season with his total home runs from 1961 to 1976 gives us a New Total Home Runs in Career of 763 for Hank Aaron; 8 more home runs than his actual career total of 755 thanks to the MLB rule change. Thus, had Aaron been able to play 162 games for the first seven years of his career, he likely would have barely had more homers than Bonds, beating him out by literally one career home run. But wait! There’s more! Remember how we mentioned that Babe Ruth was mainly a pitcher during his years with the Red Sox? What if Ruth was able to be the legendary slugger that he went on to be during the first six years of his career? Although Ruth was finally allowed to be a position player in his 5th and 6th years with the Red Sox, he still was used often as a pitcher, pitching in 20 and 17 games respectively. For the purpose of this extra add-on, we’ll recalculate Ruth’s New Home Runs by multiplying his Home Run Percentage for those years with the average New Total At Bats Per Season he had throughout the rest of his career. Let’s do it: *Note: the New Total At Bats in Season from 1920 to 1935 add up to 7,684; dividing this by 16 gives us the Average New Total At Bats in Season of 480.25, which we round down to 480* **Also, since Ruth didn’t hit any homers in 1914, largely due to his lack of games played, we are forced to assume that he still wouldn’t have played often or hit any home runs that year** Adding up Ruth’s New Home Runs in Season for his first 6 seasons (86) with his New Home Runs in Season for the remaining 16 seasons (693) gives us a New Total Home Runs in Career of 779 for Babe Ruth; 65 more home runs than his actual career total of 714 thanks to the MLB rule change and his being primarily a pitcher for the start of his career. Thus, with the information that we’ve gathered, had all 3 sluggers been able to play as position players their entire career and play 162 games each season, Babe Ruth would likely be our home run king with about 779 homers compared to Aaron’s 763 and Bond’s 762. However, Ruth’s pitching status was a decision made based on his skill at an early age, something that he could have hypothetically changed had he shown a greater aptitude for hitting rather than pitching. On the other hand, the switching of the number of games played each season was an executive decision made by the MLB and totally out of the players’ hands. It is for this reason that I believe that Hank Aaron should be considered baseball’s all-time Home Run King. Even though his new numbers only show him beating Bonds by a mere one home run, he was able to accomplish that feat steroid-free. The topic is still largely up for debate, but I hope this investigation helped to put each of the careers of the three players involved into a better perspective. As always, this post used statistical assumptions that both Ruth and Aaron would continue to hit homers at the same rate they had been doing all season in their extra games. Both players could have hit or more less homers than predicted and thus changed their hypothetical career totals. Thanks again for sticking around and giving this a look. Best wishes, Aaron Springer Sources used: https://www.baseball-reference.com/players/a/aaronha01.shtml https://www.baseball-reference.com/players/r/ruthba01.shtml https://www.baseball-reference.com/players/b/bondsba01.shtml https://en.wikipedia.org/wiki/Babe_Ruth https://en.wikipedia.org/wiki/Hank_Aaron
As we all know, the title of Major League Baseball’s all-time hit king belongs to Peter Edward Rose Sr., known as the iconic Pete Rose. “Charlie Hustle” broke the mold on September 11, 1985, with a single to left-center off of San Diego Padres pitcher Eric Show, making it his 4,1922 hit, presumably breaking the 57-year-old record of 4,191 set by the great Tyrus Raymond Cobb. It has since been discovered that one of Ty Cobb’s games was falsely recorded, meaning “The Georgia Peach” actually ended up with 4,189 base knocks and that Rose technically surpassed him on September 8 of 1985 with a single off of the Chicago Cub’s Reggie Patterson. And thus, the story goes. But does it belong? Society can go on and on about the subjectivity of the term “Hit King”. For the most part, it’s agreed that the “king” of each statistical category in baseball is the player with the most of it – Rickey Henderson is the runs scored and stolen base king, Hank Aaron the RBI king, and Barry Bonds the HR king - despite the wishes of most of baseball’s fans. This means that sitting on the throne of a statistical category is based more on the longevity and consistency of a player’s career than how good that player was at certain periods of time. One could argue that Ichiro Suzuki is the “Hit King” because, in 2004, he set the record of hits in a season with 262. During that season itself, Ichiro was the hit king of baseball. However, because of Ichiro’s ethnicity and the lack of MLB recruiting in Japan at the time, he wasn’t able to play in the big leagues until he was 27, putting him at a major disadvantage for the quest of “Hit King” (comparatively, Rose started playing at age 22 and Cobb at 18). We could go on and on about the lapses and holdbacks in different players’ careers that prevented them from being the best. Whether it be international status, an injury, early retirement, or simply the front office not calling up a player early enough, things happen that are simply unavoidable. For the most part, we can’t relish in the “what ifs?” of baseball and it is for that reason that we crown Pete Rose as our Hit King. With that all being said, I am writing today to present a “what if?” that isn’t rooted in an injury or lack of skill of a player at a young age, but rather in the basic setup of the MLB as a whole: games played. In 1961, the MLB underwent a change in both the NL and AL in which teams would begin playing 162 games a year, a schedule that is still in use today. Prior to that, starting in 1904, teams played only 154 games each, with the exception of the 1919 season which consisted of only 140 games. With Cobb playing from 1905-1928 and Rose from 1963-1986, you may be beginning to see the issue at hand. So let’s take a look. Though Cobb was supposed to play 154 games/year and Rose 162, both players had seasons where they actually exceeded this limit. Cobb played 156 games in 1909 due to tie games being called on May 31 and July 16 of that year. Though most games go into extra innings when it is tied, some games throughout history have been recorded as ties due to darkness (not so much an issue in the modern era), weather, passing curfew, or both teams running out of available pitchers. Similar instances took place in 1915 for Cobb and the Tigers on May 29 and 30, as well as in 1924 on July 10. Likewise, Pete Rose played in 163 games in 1974 when the Reds tied the Braves on September 11 (a day of the year that would become notable to him later on in his career). Though Cobb benefitted more from these “extra game” scenarios, Rose had the bigger advantage as he was seemingly able to play 8 more games than Cobb each year. Thus, for the purpose of this exercise, we will be manipulating the statistics of Ty Cobb’s hitting career to reflect if he had been given the opportunity to play 162 games each season. To begin, we aren’t simply going to calculate the stats as if Ty Cobb had literally played 162 games every year. For one reason or another, baseball players consistently miss games each year even though their teams play a full schedule. Thus we will take the percentage of the total games that Cobb actually played each year and multiply that by the new games per season of 162. Please see the table below: By adding up all of the New Games Played for each season, we get a New Career Games Played of 3,110for Ty Cobb; 76 more games than his actual career total of 3,034 thanks to the MLB rule change. From here, there are mainly two ways in which we can calculate how many additional hits Cobb would have recorded: by using his career batting average or by using his batting average for each individual season. Regardless of which way we do, however, we first must calculate how many additional at-bats Cobb would have had. For this next step, see the table below: Adding up the total of the New Total At Bats in Season gives us a New Total At Bats in Career of 12,085for Ty Cobb; an extra 651 at bats more than his actual career total of 11,434 thanks to the MLB rule change. Using the first method that we mentioned earlier, which is faster and easier albeit less accurate, we can take Cobb’s New Total At Bats in Career and multiply it by his career batting average to find his rough New Total Hits in Career (12,085 x .366 = 4423.11à4,423). This shows already that Cobb’s hit total would likely have been more than Rose’s had he had the luxury of 162 games per season (Rose finished his career with only 4,256 hits). However, since this is the less accurate way to calculate this new total (and thus less fun way), let’s try it again using Cobb’s batting average for each individual season. For this last step, take a look at the table below: For one last time, by adding up the total of the New Hits in Season, we get the more accurate New Total Hits in Career of 4,413for Ty Cobb; 224 more hits than his actual career total of 4,189 thanks to the MLB rule change. To summarize, since Cobb’s calculated total of 4,413 is larger than Rose’s career total of 4,256, we can surmise that Ty Cobb would still be the Hit King today had he been given the opportunity to play 162 games a year. As of now, we simply allow whatever player with the most of a statistic to be deemed the King of his statistical category without taking into consideration the massive implications that the 1961 increase in games per season has. Even with Cobb’s adjusted numbers, he would have still played in 452 less games than Rose (3,562-3,110) and had 1,968 less at bats (14,053-12,085), yet still had 157 more career base hits (4,413-4,256). Cobb also posted a much higher career batting average (.366) than Rose did (.303). When it comes down to the numbers, it becomes quite clear that the only reason “Charlie Hustle” ever eclipsed “The Georgia Peach” was because of his additional games per year advantage. It is with this evidence that I from now on believe that Ty Cobb, not Pete Rose, is the greatest hitter of all-time and thus should be crowned Major League Baseball’s Hit King. As with every statistical analysis, this exercise used some assumptions in its process, notably that Ty Cobb would have batted the same he was each year in all of the additional games for each year. Obviously, Cobb could have gone in a slump in his additional games or maybe even hit better than he was, but for the purpose of the exercise (and primarily due to the lack of skill/knowledge of the 2-week-in college freshman writing this), this assumption was made. I hope you all enjoyed reading this and consider adjusting your stance on the Hit King as well, although I’m sure most of you will be from Cincinnati and refuse to do so. I look forward to writing more about statistics and baseball in the future. Thanks for reading and sticking around, Aaron Springer
A final point that I forgot and would like to add: Ty Cobb would have needed just 68 more hits to pass Rose's mark of 4,256, yet would have had roughly 651 additional at bats had he been able to play 162 games each season. Thus Cobb would have only had to hit a dismal .105 in his "extra" games to break the record... |
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