Shawn Childs is a 7-figure lifetime winner in high-stakes fantasy sports. Yes, you read that right. His analytical and thoughtful approach will have you well-prepared on draft day to select sleepers before they break out! Your opponents will be jealous. You’ll be bragging all season long sitting atop the standings.
The introduction and advancement of analytics have drastically changed Major League Baseball. That’s also true in Rotisserie and fantasy baseball, where new metrics can make a huge impact on the upcoming season.
FPGscore
The development of FPGscore started with the theory around Average Player Scores. When drafting, it is challenging to determine a baseball player’s value when you have multiple positions in the fantasy baseball market, never mind numerous scoring categories. The average player theory is a way to compare a player’s value for overall impact in team-building in 5 X 5 roto formats and within the spectrum of each position or category.
Once we have a baseline of the average player, we can determine which players have the most significant edge. After establishing these scores at each position, the next step is then comparing the best option at other positions.
Each season, the player pool changes in the fantasy baseball world. Some positions will have more depth, and others will only have a few reliable options. Therefore, when a fantasy manager is preparing to do his draft prep, he wants to find each position’s hidden values. Doing this allows drafters to select the most potent options at the other positions early in the draft.
I developed a way to determine each player’s value with each category relevant to their production. For example, hitters have five offensive categories (batting average, runs, home runs, RBI, and stolen bases). Pitchers also have five categories (wins, ERA, WHIP, strikeouts, and saves).
With these scores, a fantasy drafter can quickly look at stats to see which players have the most value, either by last year’s stats or this year’s projections from any source. When using projections, a fantasy manager’s success will only be as strong as their ability to interpret information. Therefore, finding the best source for that information is essential.
Our FPGscore is built for 12-team, 5-by-5 Roto leagues with once-a-week pitching moves. I could modify these options for 10-team and 15-team leagues in the future, and we may even add bi-weekly pitching move formats.
The most challenging part for any fantasy gamer to understand is draft rankings or cheat sheets due to the underlying information behind each player’s profile. At any position in baseball, I may only like a handful of players. When I rank them, I can’t leave players I don’t like off the cheat sheet, and it wouldn’t be fair to list them poorly based on my opinion.
Here’s a look at the midpoint values in 2022 in a field of 2,388 teams in all 10 categories:
BA: .2525, R: 980, HR: 265, RBI: 954, SB: 113, W: 87, SV: 57, ERA: 3.505, WHIP: 1.164, K: 1,318
Average Draft Position
The fantasy market uses ADPs (average draft position) to prepare for the upcoming draft season. ADPs give drafters a feel for a player’s value in the open market. It is a great tool, but a fantasy player must understand the value of the information.
ADPs from mock drafts have less merit as all teams don’t complete their drafts, and many drafters may lose interest at some point during the draft. The best information in fantasy baseball comes from drafters playing for real money or fantasy managers competing in a real league that will be played out during the season.
Value of FPGscores
FPGscore can work with any projections to deliver results. First, I research all 30 baseball teams. I then do our team profiles for each team’s projections. With this information, I provide rankings based on the FPGscores. Also, I can back-check the results from the previous season to see how each player stacked up against their competition. The goal is to compare players with different skill sets and find which options have the most value to a fantasy team.
At the same time, I can deliver weekly rankings based on playing time and opportunity. I break the season into 27 weeks (two half weeks – Week 1 and the All-Star break) to develop the weekly results. If a player is projected to play in seven games, he’ll have a better chance to produce stats in the counting categories. More playing time doesn’t necessarily mean he’ll have a higher score than a player with a much higher skill set with five games.
Note: FPGscore equations are adjusted each season for the current playing field in major league baseball. If home runs decline, a big power hitter will be rewarded for his edge in home runs. Likewise, an elite base stealer will have a higher impact in the stolen base category if steals are scarce.
Wins Category
The midpoint for wins in 2022 was 87, divided into nine pitching slots to come up with 9.111 wins per pitcher. I then used the overall standing from a league with 2,388 teams to determine the points gained for a win or lack of a win. First, I used +/- 750 spots in the overall standing to get a range of points gained or lost from the midpoint of wins. It was amazing to see 1,575 teams fall between 76 and 96 wins. Next, I divided 1,500 overall points by 19 wins to find that each win was worth 78.95 overall points. There were 199 leagues in this competition, so each win within a single league environment was worth .39673 league points.
ERA and WHIP Categories
The midpoint in innings for the ERA and WHIP categories rose to 1,313 in 2022 (1,344 in 2019 and 1,269 in 2021). ERA (3.791) and WHIP (1.188) showed improvement last season. I then subtracted the innings pitched from 1,313. Next, I multiplied that number times (.3894 = 3.505/9). This data gave me the total number of runs allowed for the remaining innings for the midpoint in ERA by inning. I then added the total number of runs allowed by each starting pitcher and divided that number by 1,313 innings. This result delivered each pitcher’s +/- impact based on the number of innings pitched or projected to pitch. Next, the range of 1,500 league points was divided by a gap of .550 in ERA (3.795 – 3.245). This result (2,727.2172) was then divided by 199 leagues. Finally, I used a -13.705 data point to show that a lower ERA awarded more points.
I repeat this same process for WHIP. The range of overall points (1,500 spots) was divided by .099 (gap in WHIP from 1.115 to 1.214). I then divided it by 199 leagues to deliver -76.138. Again, I used a negative number as a lower WHIP is the desired result.
Strikeouts
For strikeouts, the midpoint total was 1,318 strikeouts. Pitchers aren’t equal in Roto formats, but I still need to divide 1,318 by nine pitching spots. The average sum of strikeouts per pitcher came to 146.44. The range of strikeouts for 1,500 teams came to 218, with a low of 1,190 at the 1,944th and 1,408 at the 444th positions. Each strikeout was worth 0.034577 points in the standing after dividing by 199 leagues.
Saves
This midpoint for saves was 57 in this event. A fantasy manager typically will get saves from two to three roster spots in their starting lineup, but we need to base the target goal on nine pitchers leading to a negative score for each starting pitcher in saves. Over 1,500 spots in the overall standings in a 2,388-team event, there was a difference of 39 saves. This total came to 0.193274 points in a single league per save. Many fantasy managers play the save category differently, creating a wide range of results. A format with an overall prize does lead to more teams competing in this category.
Batting Average
I used the same theory for ERA and WHIP for batting average. Looking at the starts for all 2,388 teams, I determined that I needed 7,275 at-bats and a batting average of .2525 to be at the midpoint in 2022. For each player, I subtracted their at-bats from 7,275, then multiplied the result by .2525 to give the total number of hits to deliver a midpoint batting average. I then added the player’s total hits to this number and divided that total by 7,275 at-bats. These results gave me the impact of each player as far as +/- in batting average. The range of 1,500 spots in the standing was 0.0129 points in batting average or about 93.8 hits over 7,275 at-bats. So, 1,500 divided by .0129 divided by 199 leagues = 584.32 points for batting average.
Runs
The midpoint for runs was 950. The range was 128 runs over 1,500 spots in the standings, which delivered 0.0589 points per run in a single league.
Home Runs
The midpoint for home runs was 265. The gap between 444th and 1944th place in a 2,388-team format was 57 home runs or 0.1125 points per home run in a single league.
Runs Batted In
The midpoint for RBIs in 2022 was 954. The difference in 1,500 points in the overall standing in RBI was 138 RBIs. This number worked out to .0546 points per RBI in a single league.
Stolen Bases
A team needs to get 113 stolen bases to finish at the medium point last season. The gap between 1,500 spots in the overall standing was 43 stolen bases leading to each steal being worth 0.1753 points in roto formats.
Using these totals, a fantasy manager can easily see which players had the most value last season. It is a tool that also helps you make future decisions. The real trick is to create these values for this year’s projections. A drafter can make better draft decisions by understanding the player pool and each player’s value within each category. Here’s a chart for both batters and pitchers to show power points gained or lost in each category within a league environment in 2022. Here’s a table to help understand the gains and losses in FPGscores in each category:
To add some food for thought about values in each category, a player would need to hit these stats to gain three points in each category in a league environment >>> .321 BA, 119 runs, 46 home runs, 123 RBIs, and 25 stolen bases on the hitting side. Likewise, pitchers would need about 17 wins, 22 saves, 1.55 ERA, 0.81 WHIP, and 233 strikeouts to gain about three points in each category. Note: All pitcher gains or losses in ERA and WHIP are based on 146 innings pitched.
Draft Decisions Trade-offs
Once I have each player’s projections calculated for FPGscores, I can compare all players’ values. For this information to succeed, I must compare players at like positions to help identify potential edges and sleepers.
ADPs and a player’s draft value help fantasy managers make trade-off decisions within drafts. Once drafters have this information, they must decide how much they trust or agree with a player’s projections. The next step is comparing that player with other players at the same position in the projected ADPs.
The bottom line is that a fantasy manager is trying to gain an edge with each of his first few picks in the draft while filling as many categories as possible. Each decision takes a fantasy team in a different direction.
I also had access to multiple other events with large amounts of teams competing for an overall championship. The information I used is from a league with once-a-week transactions for pitching.
The next step when making decisions for 2023 comes from reviewing projections with FPGscores. The second level of information helps put a player in perspective based on his career path and current opportunity. The first set of forecasts won’t be released until each major team has been researched from the second week in January til the third week in February.
Average Hit Rate (AVH)
After finishing the research for all 30 teams for the 2023 baseball season, I’ll swing back to the average hit rate stat I use for a reference to help determine the direction of a player in home runs.
Slugging percentage has been the standard for many years in baseball to show a player’s potential in power. Last year the major-league average in slugging percentage was .395 (.411 in 2021, .415 in 2020, .435 in 2019, and .409 in 2018).
Average hit rate (AVH) = singles + doubles (each base X 2) + triples (each base X 3) + home runs (each base X 4) divided by hits or total bases divided by hits.
In 2022, 24 players hit 30 home runs or more. They had a range of .455 to .686 in slugging percentage compared to 1.75 to 2.21 in average hit rate. Here’s a list of batters that hit 30 or more home runs last season:
Last year 93 players had a minimum of 500 at-bats; 20 of those hitters had 30 home runs or more. Every player had an average hit rate of 1.75 or higher. In comparison, 10 batters with 30 home runs and at least 500 at-bats had a slugging percentage under .500.
To help understand my thought process when using this data in my player research, I’ll look at a couple of players to show how I use this data.
In 2019, Jeff McNeil had a breakout season in power (23 home runs and 75 RBIs over 510 at-bats). His average hit rate was 1.673, with a slugging percentage of .531. Based on his slugging percentage, he appeared on a path to be a 20-home-run hitter going forward. However, his average hit rate suggested a weaker power swing.
Over the past three seasons, McNeil only has 20 home runs over 1,102 at-bats while seeing three years of regression in his average hit rate (1.456, 1.433, and 1.391). Additionally, his slugging percentage over this span has been .454, .360, and .454. In essence, the length of his hits as far as bases have diminished significantly since 2019, highlighted by his HR/FB rate (5.4) in 2022 being almost one-third of his breakout season in power (15.4).
Ultimately, anyone drafting McNeill in 2023 and hoping for a run at 20 home runs needs another viable (more loft, a livelier baseball, or more strength) to change for him to reach that plateau. At this point in his career, 12 home runs for McNeil would be a fair baseline to use when building his outlook.
When the Kansas City Royals called up Vinnie Pasquantino in late June, the fantasy world expected him to hit the ground running in power. He finished 2022 with only 10 home runs and 26 RBIs over 258 at-bats, highlighted by a weaker rating in average hit rate (1.526) while posting a .450 slugging percentage. Over his three seasons in the minors, Pasquantino posted a 1.951 average hit rate. He looks poised to hit 30 home runs this season while looking undervalued in drafts.
Average hit rate tells a pretty good story. I would like to see a player adding more length to his hits in the ideal situation. Any player with an average hit rate of 1.75 or higher has 30-home run power with more than 550 at-bats. A Judy-type player (all speed and no power) will have an average hit rate under 1.35.
The goal is to glance at a player’s average hit rate and home run total to get a feel of a player’s upside in power. Understanding each player’s ground ball, flyball rate, and hard-hit rate is also imperative. A change in swing path could lead to a massive jump in home runs.
Slugging percentage doesn’t tell the same story for me when studying baseball players. If a player has a slugging percentage over .500, it doesn’t necessarily mean a player is a 30-home-run hitter.
Defining a player’s direction in fantasy baseball is the key to a winning season. Ideally, a fantasy manager needs to identify a player with underlying metrics that point to a breakout season when added to a better opportunity in playing time or an improved slot in the batting order. Average hit rate is an essential tool for me and one I hope you incorporate into your research plan in the future.
Contact Batting Average (CTBA)
A popular stat for fantasy baseball managers over the last decade is BABIP (Batting Average Balls in Play). Unfortunately, this data point isn’t a good reference for determining a player’s batting average. Each player in baseball has their own skill set and baseline for BABIP. Like batting average, this stat can have a wide range from season to season for each player. What looks suitable for one player in one season may be bad for another player in the same year.
The bottom line is that if a player hits the ball hard, he will get more hits. With poor contact, a hitter will make easier outs.
My best example of this is Barry Bonds. He has a career .285 BABIP while hitting .298 in his major career. In essence, his low BABIP was due to a high volume of home runs (762), which is the part that bothers me the most. Why are we discounting the hardest-hit balls? If a player hits a line drive off the centerfield wall for a hit, the defense has no chance to catch the ball. The same goes for a ball over the fence. Therefore, I decided to go against the grain in this area. I came up with CTBA (contact batting average). I want to know what a player hits when he makes contact with the ball. CTBA = Hits/At-bats minus strikeouts. Looking back, I should add back sacrifice flies.
Barry Bonds had a career contact batting average of .353 (.350 with San Francisco). From 2001 to 2004, he hit .328, .370, .341, and .362, with a contact batting average of .408. Over this span, Bonds had 775 walks and 438 strikeouts over 1,642 at-bats. His BABIP was .303 during this stretch.
When looking at Mike Trout’s career, you can see a high BABIP in some seasons (.383, .372, .349, .344, .371, .318, .346, .298, .300, .456, and .323). He’s had an elite contact batting average (2012 – .433, 2013 – .419, 2014 – .414, 2015 – .412, 2016 – .420, 2017 – .394, 2018 – .424, 2019 – .391, 2020 – .392. and 2022 – .415) every year (I left 2021 out due to him only playing in 36 games). His CTBA shows his explosiveness in batting average each year, while his BABIP had a much wider range of value while bottoming out in 2019 (.298). Ultimately, Trout has batting title upside based on his CTBA if his strikeout rate shrinks. He has strength in his BABIP (.344) and CTBA (.413) in his career.
For comparison, Ichiro Suzuki hit .311 in his career with a BABIP of .338. His contact batting average finished at .349, almost matching Barry Bonds (.353).
My goal with CTBA is to determine a better range for batting average. Most of us fear high strikeout batters as they can kill us in batting average, but players with an elite contact batting average can overcome some of this downside while also having a chance to lower their strikeout rate.
The major-league average for CTBA in 2022 was .323 (.330 in 2021 and .332 in 2020), which makes sense. It tells us that about one out of every three balls put in play was a hit.
In my early development as a fantasy baseball player, I used Ron Shandler’s Baseball Forecaster to do my research. He had a stat called contact rate (at-bats minus strikeouts/at-bats). When using this data, it helped to avoid high strikeout batters that invited batting average risk. But, at the same time, it did cause me some confusion as to what type of hitter had the most upside in batting average.
By looking at Miguel Sano’s major league profile, he has a .234 batting average in his career with the Twins with an insanely high strikeout rate (36.5 percent). Unfortunately, his contact rate is only 63.5 percent, which screams disaster downside in batting average.
On the flip side, his contact batting average in the majors is an impressive .400, with a high of .446 in 2017. If Sano lowered his strikeout rate to 30% while maintaining his career contact batting average, he would become a .270 hitter.
Jose Ramirez worked his way to a top-five fantasy player this draft season. In 2016 and 2017, he hit .315 over 1,150 at-bats with a BABIP of .326. For someone relying on BABIP to make their evaluations for batting average with Ramirez over the next five seasons (.252, .256, .294, .256, and .279), they may conclude that he was unlucky in some of these years with the hopes of him becoming a .300 hitter again.
The truth is Ramirez had a change in his swing path in 2018 that carried over in his following seasons. He became a fly-ball hitter (over 45%), leading 144 home runs over his last 2,432 at-bats. His contact batting average over this span was .318, which was his ceiling for batting average. In 2016 and 2017, Ramirez hit .355 when putting the ball in play. His regression in batting average came from making easier outs via fly balls, as his strikeout rate (12.2) has been low for his whole career.
Over his first three seasons with the Padres, Fernando Tatis Jr. has an impressive contact batting average (.426). His strikeout rate (27.6) remains well above the league average, but he has had success in batting average each year (.317, .277, and .282). In his rookie season, Tatis finished with an exceptional BABIP (.410 – .473 contact batting average). Over the previous two years, his BABIP slipped to .306 and .324. His regression in 2020 came from a lower strikeout rate (23.7) and last year from more balls leaving the park. Tatis hit .380 and .415 when putting the ball in play over the past two seasons.
With more experience, Tatis should naturally have growth in his approach with fewer strikeouts and push his batting level to an elite level. In addition, about 30% of his fly balls leave the park, creating a home run beast.
As a fantasy manager, I want the players who have the best chance to hit the ball hard, leading to home runs and production in RBIs. I must walk a fine line when deciding between high strikeout batters to limit the damage in batting average. Contact batting average is a way to see who has the best chance to get a hit when they put the ball in play, which isn’t the case for BABIP. A high CTBA and improving approach signals a potential breakout player.
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