Getting into the advanced side of baseball is not easy. You have to start slow, and it seems like there’s a mountain range of statistics and websites that are accessible on the internet. There are hundreds of books, different viewpoints, and articles that prove the value of any stat. In this article, I will try to teach you the websites to use, the basic statistics that should be known, and analytics to judge players with.

Websites

Baseballreference.com, baseballsavant.com, fangraphs.com

The “three musketeers” of baseball websites are Baseball Reference (bREF), Baseball Savant, and Fangraphs. They’re all used for different purposes – Baseball Reference is more for base-level statistics, Savant is used for predictive measures, and Fangraphs is a mix of the two. The real starting point for baseball fans is Baseball Reference; the statistics there are enough to give a brief introduction to player value and evaluative numbers.

Baseball Reference

There are two types of player pages on Baseball Reference: hitting and pitching. Both pages have completely different statistics, so here are some introductory statistics for both.

Pitchers

Stat#1 (ERA): Earned Run Average, or ERA, is one of the most used statistics in baseball. It’s the primary evaluator of pitcher value across a season or career, and it uses total earned runs (ER) and innings pitched to determine how many runs a pitcher gives up per 9 innings (1 game). A perfect ERA is 0.00 and the maximum ERA has no limit, but in the MLB, you’ll rarely see an ERA over 6 runs per 9 innings (6.00). Shown through Paul Skenes’ page above, his 2025 ERA is 2.03, the best mark in the league, but if a pitcher has an ERA under 4.00, they likely provide enough value to their team to stay in the MLB. Pitchers with ERA’s under 3 are considered elite and worth immense value to their team, as the MLB average ERA in 2025 is 4.17 runs per 9 innings.

Stat #2 (ERA+): Earned Run Average plus, or ERA+, adjusts a player’s ERA to the MLB average during the current year, with 100 considered average. This year’s league average ERA of 4.17 equates to an ERA+ of 100; so anything under 100 is considered below average and vice versa for an ERA+ over 100. Unlike normal ERA, ERA+ accounts for shifts in league run scoring, and an average ERA+ in 2025 is different compared to the average ERA+ in 2024. This is best shown through Paul Skenes’ page above; although his ERA in 2025 is higher than his 2024 mark, his ERA+ is still higher due to a rise in league hitting this year.

Stat#3 (WHIP): WHIP is an abbreviation for Walks and Hits per Inning Pitched. Per its name, WHIP is calculated by adding a pitcher’s total walks and hits, then dividing the resulting number by innings pitched. It evaluates how good a pitcher is at limiting baserunners, and eventually, runs. The lower a pitcher’s WHIP is, it is assumed that they don’t give up a lot of runs. League average usually sits at 1.3, or 1.3 walks & hits per inning, and the elite starters and relievers usually tend to have a WHIP under 1.000. You’ll rarely see any pitcher in the MLB have a WHIP over 1.700.

Stats(s)#4: Per 9’s: /9 stats are the amount of something that a pitcher gives up per 9 innings pitched. For example, if a pitcher has a 1.0 HR/9, then they give up 1 home run per 9 innings. It’s that simple. The harder part about these stats is judging what a good pitcher tends to put up in the /9’s, but a good benchmark for an elite, well-rounded pitcher is Paul Skenes. In the page above, his Hits per 9 (H/9) sit at 6.5, his Home Runs/9 (HR/9) sit at a league-best mark of 0.5, and his walks/9 and strikeouts/9 sit at 2.1 and 10.4, respectively. All of those marks are considered very good, and although you could see each statistic improve via elite strikeout pitchers or control freaks like Aroldis Chapman and Nick Martinez, they’re usually only present in specialists or relievers.

Stat#5 (FIP): Fielding Independent Pitching, or FIP, is another primary evaluator of pitcher value and performance. Similar to Earned Run Average regarding its scale of 0.00-infinity, FIP values how a pitcher performs without defense helping him. Crucial statistics used in FIP’s calculation are home runs, strikeouts, and walks, and if a pitcher leaves less plays to be determined by fielders, they will have a better FIP than a pitcher that relies heavily on their team’s ability to make plays behind him. FIP is on the same scale as ERA, so a FIP under 3.00 is great, league average is around 4.00, and you’ll rarely see a pitcher with a FIP over 5. If someone has a FIP under their ERA, it suggests that his fielders aren’t helping him very much and vice versa for someone with an ERA under their FIP.

Hitters

Stat#1 (BA): Batting Average, or BA, evaluates how often a batter is able to get a hit. Calculated by dividing hits by At-Bats (AB, plate appearances that either end up with a hit or an out, no walks), a perfect BA would be 1.000, the worst possible batting average would be .000, and the league average is around .245 (a hit in 24.5% of AB’s). A good batting average is around .260-.280, and anything above is considered elite, like Aaron Judge’s batting average of .327 above. The maximum BA is usually .340 now, as getting a hit above 40% of the time has been reached one time in the history of the MLB and league batting average has regressed by 15% since 2000.

Stat #2 (OBP): On-Base Percentage, or OBP, shows how often a player is able to get on base – whether it’s a hit, walk, or a hit by pitch. It’s also on the 0-1.000 scale, and is usually a bit higher than BA at a league average of .315. The best hitters in the league, like shown above with Aaron Judge, tend to have OBP’s around .340, and the truly elite hitters like Aaron Judge have OBP’s of near or above .400 (On base 〜 40% of the time).

Stat #3 (SLG): Slugging percentage, (SLG), is a sort-of power evaluator for hitters. It’s calculated by taking a player’s number of bases that home runs, triples, doubles, and singles give, (4, 3, 2, and 1, respectively), adding all of their hit results up, then dividing them by the amount of at-bats that a hitter got. For example, if a player hits a home run in his first at-bat of the season, his slugging percentage will be 4.000 because he obtained 4 bases on the hit. Since a home run is the highest number of bases someone can get per at-bat, the max SLG is 4.000 and the minimum is 0.000. This stat is important, because unlike batting average, SLG weighs more effective contact like doubles, triples, or home runs higher than singles. Therefore, a hitter that has a high BA and relies on singles will have a lower SLG than someone who has a lower BA but hits a ton of home runs and doubles. League average SLG sat at .404 this year – the most elite power hitters in the game can have slugging percentages in the .600s, and a bad SLG is around .350.

Stat #4 (OPS): OPS, or On Base Plus Slugging, is a stat that tries to mesh power and the ability to get on base. Its calculation is simple – it’s the addition of OBP and SLG into one single stat. Since the maximum values for SLG and OBP are 4.000 and 1.000 respectively, the maximum OPS that a player can have is 5.000, with the minimum then being 0.000. The MLB average OPS in 2025 is .744, and the general rule of thumb for good/bad OPS’s are .800 and .700. Anything over .800 is considered great, and any player with an OPS below .700 isn’t the greatest. Shown in Aaron Judge’s page above, his OPS this year sits over the 1.000 mark, which is absolutely astounding and worth MVP votes.

Stat #5 (OPS+): OPS+, exactly like ERA+, adjusts a player’s OPS to the league average rate across the course of an entire season. You’ll notice that any stat with a plus at the end will indicate something with an adjusted average of 100, with any number over 100 being above average and vice versa for numbers below. Since the league average OPS in 2025 is .744, that’s set as baseline of 100, and the best hitters in the league tend to have OPS+’s of 120 and above. Aaron Judge’s 2025 OPS+ of 209 is an anomaly, that means that his OPS is 109% better than the average hitter.

Now that we’re done with all of the basic statistics on Baseball Reference, there’s one more number to describe. This next one is called Wins Above Replacement, or WAR.

What is “WAR?”

One of the ultimate performance evaluators in sports, WAR (Wins Above Replacement) takes a players’ performance from multiple areas of the game and aggregates them into a single stat, which judges the amount of wins that a player gives to their team compared to the “replacement-level player” (=0.0 WAR). Anyone in the negatives is detrimental to their team’s win total, someone around 1.5-2 WAR is considered league average, a player with 4 WAR is likely an all star, and anyone with above 6 is in the conversation for offseason awards. Shown with the 2 players above, Paul Skenes and Aaron Judge, they both lead their respective divisions in WAR (highlighted in bold on stat sheets), which means that both players are among the best in the world on their respective sides of the ball. It’s important to note that there are two types of WAR: bWAR (baseball reference WAR), that depends more on basic statistics, and fWAR (fangraphs WAR), that delves into the advanced numbers. At higher levels of analysis, it’s important to recognize the factors that make both models fluctuate, but at base levels of analysis, it doesn’t matter which one you look at.

Baseball Savant

My personal favorite website to evaluate players; Baseball Savant has it all. From metrics to analytics to splits, it’s the most useful website in my opinion in which data is easily accessible and available to be consumed. Because there’s so much information to be accessed, I’m only going to be delving into the analytics on the front page of the typical Savant page.

Like Baseball Reference, there’s two possible pages that you can look at: hitting and pitching. Both have completely different stats and meanings, and to keep the simplicity of it all, instead of doing two new players – we’re going back to Aaron Judge and Paul Skenes. Keep in mind that most of the numbers that I’ll be showing deal with percentiles – 1 is the worst, 100 is the best.

Pitchers/Hitters

Analytic #1 (xERA, pitchers only): Expected Earned Run Average, or xERA, is the aggregation of all the walks, strikeouts, and batted balls that a pitcher has into a predicted ERA. It’s important to know that a lot of the stuff on Baseball Savant depends on the data that cameras around the field give them, which gives a lot of the predicted outcomes that arrive with expected numbers. xERA’s on the same scale as ERA, and as you can see with Paul Skenes above, his xERA is in the 97th percentile (2.59), among the best in the league. Compared with his 1.96 actual ERA, it suggests that he got a little lucky with his batted ball results compared to the predicted outcomes of them.

Analytic #2 (xBA): Expected Batting Average (xBA) is exactly what it sounds like, the predicted batting average that pitchers and hitters get based on the batted balls that they give up/hit. For example, if a pitcher gives up hard-hit fly balls into the outfield but doesn’t strike out a lot of players, it’s expected that he’ll give up more hits than a pitcher who strikes out a lot of people but gives up weak contact. A great xBA for pitchers is around .200, while a bad xBA for a pitcher would be anything above .260. For hitters, it’s much of the same – if they hit the ball harder and more often, they’ll have a higher xBA. With Aaron Judge, he is one of the best players in the league considering xBA at a .315 mark.

Analytic #3 (AVG EV): Average Exit Velocity, or AVG EV, is pretty simple. It’s literally the average speed of every batted ball that a player hits or gives up in a year. I’m not entirely sure what a great average is versus a bad average, but as shown in Paul Skenes’ page above, his AVG EV stood in the 87th percentile at an average of 87.1 mph and Aaron Judge was in the 100th percentile at 95.4. Remember, with pitchers and hitters, their stats are usually flipped. A high AVG EV is good for hitters, but not good for pitchers.

Analytics #4-5 (Chase & Whiff %): Like average exit velocity, not much explanation is needed here. Chase% is the proportion of pitches outside of the zone that result in a swing, and Whiff% shows the percentage of times that a batter misses the ball completely during a swing. These numbers represent a pitcher’s ability to be elusive, miss bats, and trick hitters into poor decisions. For hitters, it represents their ability to get fooled by other pitchers and not swing and miss.

Analytic #6 (Barrels): “Barrels” are a representation for how often the best contact results are reached. For pitchers, it’s important to give up less barrels, and for hitters, they’re trying to get as many as possible. If a pitcher gives up a ton of barrels, it’s assumed that they’ll give a lot more runs than a pitcher that gives up a few barrels, and for hitters, it’s important to hit as many barrels as possible.

Ok. That was a lot. I’m not going to delve into Fangraphs in this article – it just contains a lot of what I already showed today. If you were interested in this article at all, I strongly recommend looking at all three websites and seeing if there’s anything that pushes your interest further – I’ve found that it’s easy to talk about these stats to someone, but it becomes difficult to explain to them what they actually mean, so any feedback on my explanations would be greatly appreciated.