Counting stat equivalents (CSE) can help fantasy players track their ratio categories throughout a draft and during the season in a manner similar to regular counting stats. This is my attempt to share a method I have created for converting ratios to CSEs. I'll use batting average to illustrate this process, but the same process can be used for pitching ratios, which I will touch on in closing.

The approach to drafting counting stats is simple. If you estimate that you will need 300 home runs to win the category, then each player you draft is projected to get you a certain portion of that estimated need. For example, Vladimir Guerrero Jr. is projected by Steamer to hit 47 home runs, which is the highest home run projection of any player. If he hits those 47 home runs, he gets you 16 percent of your goal in that category (47/300 = 15.6%).

The thing about counting stats that fantasy players can get away with ignoring for the most part is that each category has a floor. If the last place team in the home runs category in a 15-team league is expected to hit 200 home runs, then each hitter on a standard sized roster with 14 hitters needs to hit 14 home runs before they start helping the team in that category (200/14 = 14.2). A player that hits only 8 home runs is a negative in the category. While this is all true, it is not terribly important to pay attention to in counting stat categories because teams need chase only the raw total of the 300 home run target. Who cares if one player only hits 8 home runs if your team projects for more than 300 home runs total? It's very easy to track the projected totals relative to the target.

__Ratios as Counting Stats__

The ratio categories are different. Batting Average, Earned Run Average (ERA), and Walks+Hits/Innings Pitched (WHIP) don't work the same way. It is much more difficult to keep track of ratios during a draft or an auction. Most fantasy players make no attempt to do this. To the extent that they do, they "keep an eye" on their ratios as they go. This can be deceptive and misleading. If, for example, a fantasy player drafts hitters with any of the projected batting averages of .291, .267, .311, .288, .307, .269, .258, .285, or .278, what does that really tell the drafter? Which batting average is best? It's not as obvious as it seems.

It is impossible to answer the question of which batting average is best for a fantasy team because we have not yet taken volume (at-bats) into account. All batting averages are not equal in roto fantasy. Volume matters far more than fantasy players realize. To understand which of those batting averages helps a fantasy team the most, you can convert batting average to a CSE and track it throughout the draft, much as you would with home runs.

__Creating the CSE Denominator__

First, you need to create your target and floor. You can simply use first and last place category statistics from individual leagues, or percentiles from overall contests. I like to look at overall contests and use the 85th percentile as a category winning target and the 5th percentile as a category floor. Those will typically capture first and last place category performance, respectively, and eliminate outliers. The purpose of the floor target (5th percentile) is to account for the fact that any batting average below that threshold will drag the fantasy team's batting average down towards last place, whereas anything above it can be considered a net positive for counting purposes. If you were to simply use zero as the floor, as we do in regular counting stat categories, you would find a situation in which a .220 hitter with a ton of at-bats would be more valuable in CSE than a .250 hitter with considerably fewer at-bats, but we know that is not true, so we set an artificial floor to create a threshold at which positive value begins. Here are those thresholds for NFBC Draft Champions from 2021:

Percentile | Batting Average |

85th Percentile | .263 |

5th Percentile | .243 |

To convert batting average into a counting stat, you need those percentile targets as well as some constant number for team at-bat totals. You can use the mean or median at-bat totals in an overall contest or an individual league. I like to use a number on the higher side because I always anticipate aggressively logging at-bats to maximize counting stats, so I use the 85th percentile for at-bats, just like I do for my batting average target. In 2021, the 85th percentile in at-bats for NFBC Draft Champions leagues was 7233.

Once you have your percentiles and at-bats targets, you simply multiply at-bats and the batting averages, the sums of which are your target and floor numbers used to create your counting stat equivalents (CSE).

At-Bats (AB) | Batting Average (BA) | Sum |

7233 | .263 | AB x BA = 1,902 |

7233 | .243 | AB x BA = 1,758 |

Next, subtract the 5th percentile sum (1,758) from the 85th percentile sum (1,902). In this case, the new sum is 144 (1,902 - 1,758 = 144). In this exercise, 144 is the same as the aforementioned home run target of 300. It is what a team needs in order to win the batting average category and it will be used as the denominator when converting individual batting average projections to CSE. The numerator will be based on individual player calculations.

__Creating the CSE Numerator__

Obtaining a player's batting average CSE is a two step process. Let's use Trea Turner's Steamer projection as an example. Steamer projects Trea Turner to hit .291 across 606 at-bats.

First, you subtract the batting average floor that we already set (.243) from the player's projected batting average (.291).

.291 - .243 = 0.048

Then, you multiply that sum by the player's projected at-bats (606).

0.048 x 606 = 29.088

Now we have our numerator. Divide the numerator by the denominator we created earlier (144), and you now know the player's CSE, which is easily converted to a percentage of the total target by moving the decimal two spots.

29.088 / 144 = .202 (20%)

When we complete this exercise, we can now see that Trea Turner's batting average CSE gets us 20% of the way to our team's batting average target, much like Vladimir Guerrero Jr's 47 home runs in the opening example will take our team 16% of the way to our home run goal.

__Additional Examples__

Let's go back now and look at the question from earlier ("If, for example, a fantasy player drafts hitters with any of the projected batting averages of .291, .267, .311, .288, .307, .269, .258, .285, and .278, what does that really tell the drafter?")

Those batting averages are actually the projected batting averages of the nine hitters being taken in the first round of NFBC Online Championships over the last month. Here is a table showing their projected batting averages, plate appearances, at-bats, and CSEs using the method I've laid out:

Player | PA | AB | AVG | CSE | % (of Target) |

Trea Turner | 672 | 606 | .291 | 0.202 | 20.2% |

Jose Ramirez | 663 | 570 | .267 | 0.095 | 9.5% |

Juan Soto | 672 | 522 | .311 | 0.246 | 24.6% |

Bo Bichette | 668 | 610 | .288 | 0.190 | 19% |

Vladimir Guerrero, Jr. | 656 | 566 | .307 | 0.251 | 25.1% |

Bryce Harper | 662 | 537 | .269 | 0.097 | 9.7% |

Shohei Ohtani | 657 | 554 | .258 | 0.057 | 5.7% |

Ronald Acuna Jr. | 639 | 533 | .258 | 0.055 | 5.5% |

Kyle Tucker | 605 | 537 | .278 | 0.131 | 13.1% |

Several interesting things jump out from the table above. First, note that Vlad Guerrero Jr projects to help a fantasy team's batting average category more than Juan Soto does despite a lower projected batting average. This is easy to understand when you consider Juan Soto's incredible walk rate (Steamer Projection: 20.4%), which lowers the volume of his at-bats. Even though Juan Soto is projected for a greater number of plate appearances than Vlad, Vlad's extra 44 at-bats over Soto are enough to make his batting average projection slightly more helpful.

The second thing that jumps out is how incredibly beneficial a high batting average and high volume of at-bats can be in the batting average category. Vlad or Soto, alone, do a quarter of the work for you in winning the category so long as you don't draft it down (discussed below). On the other end, while the .258 projections for Ohtani and Acuna are not much higher than our floor of .243, the sheer volume of at-bats makes them fairly helpful.

That's all fine for the first round. Let's look at some of the more average players. The following five players are the first hitters currently being drafted after pick 300 in NFBC Online Championships:

Player | PA | AB | AVG | CSE | % (of Target) |

Max Kepler | 564 | 486 | .234 | -0.030 | -3% |

Anthony Santander | 555 | 511 | .251 | 0.028 | 2.8% |

Nick Madrigal | 420 | 382 | .297 | 0.143 | 14.3% |

Brandon Nimmo | 619 | 510 | .259 | 0.057 | 5.7% |

Gio Urshela | 483 | 444 | .260 | 0.052 | 5.2% |

This table illustrates how great the drop-off is from the valuable players in the first round to those post-300. Even if you draft Juan Soto with CSE that gives you 24.5% of your target, you need a lot of pieces later to make up the other 75%. Like any other counting category, you have to make the pieces fit. Nick Madrigal's batting average is quite valuable, but he doesn't do much else. Max Kepler hits home runs and will steal a few bases, but his negative impact on batting average is going to make you work that much harder to hit your CSE batting average target. Ratios are unique in that the bad ones work against you in a way that low counting stats do not, which is a good segway into sub-floor batting averages.

__Joey Gallo's Own Section__

Last month, I tweeted the following:

People don't like when you speak ill of Joey Gallo, but this tweet was a product of the analysis laid out in this article.

Player | PA | AB | AVG | CSE | % (of Target) |

Joey Gallo | 633 | 519 | .208 | -0.126 | -12.6% |

Of course, a player cannot hit negative home runs, but they can drag your batting average in the wrong direction. Imagine if a player could hit -26 home runs and you had to make those up on the rest of your roster. Is there a scenario in which you draft that player?

You simply cannot compete to win (or compete at all...) in the batting average category with the lowest of batting averages fueled by full at-bats. Sure, you could pair him with Nick Madrigal (see above), but what you've done in the process of using Nick Madrigal to turn Joey Gallo into a last place batting average team (as opposed to to 12.6 *below* what is required for last place) is to also turn Nick Madrigal into a last place batting average drag too. Congrats. Nick Madrigal doesn't do anything else well either. He's simply an extra spot on your roster being used to bring Joey Gallo's batting average impact up to par with the last place floor. It is not a winning strategy.

__Catcher Impact__

When I sent the Joey Gallo tweet, a smart (alec) person replied with "Now do Mike Zunino," which actually helps illustrate this exercise really well. Here's Mike Zunino along with a few other end-game catchers and their steamer projections converted to CSEs.

Player | PA | AB | AVG | CSE | % (of Target) |

Mike Zunino | 312 | 278 | .204 | -0.075 | -7.5% |

Carson Kelly | 402 | 348 | .239 | -0.009 | -0.9% |

Max Stassi | 386 | 341 | .223 | -0.047 | -4.7% |

James McCann | 366 | 330 | .229 | -0.032 | -3.2% |

Tucker Barnhart | 345 | 305 | .229 | -0.029 | -3% |

That Zunino effect hurts, to be sure. But as the table shows, despite Mike Zunino's lower batting average projection than Joey Gallo's, his negative impact is slightly more than half of Joey Gallo's negative impact. It's simply a matter of lower volume and he doesn't hurt your team nearly as much as Gallo does. Catchers in general don't do as much damage as other players because they log fewer plate appearances. If you are going to roster a batting average drag (which I don't recommend), catcher is where you want to do it.

__Batting Average CSE Tracking Tool__

*Update (3.25.22): I went ahead and created a tool for tracking batting average CSE during a draft. See the page for it with download link *__here__*. If enough people email me and ask, I'll make one for the pitching ratios and post that one too.*

__Conclusion__

People don't pay nearly enough attention to ratios when drafting, particularly how volume affects ratio categories. And though this illustration uses batting average, you can (and I do) do the same thing with ERA and WHIP. The only things done differently in the pitching categories are to set the "targets" as the lower numbers and the "floors" as the higher numbers (because, obviously, you want lower ERA and WHIP just like you want a higher AVG) and use innings pitched instead of at-bats for the measure of volume.

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*Thanks for reading!*
*- Russell*

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