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For those that enjoy reading, seeing visuals, and having immediate access to data sources that are used. The frequency of posts varies greatly and merely depends on when I have an idea come to mind and when I have the time to address it. Feel free to contact me with any blog ideas or desires.
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
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