Did you roster any 4.33 points-per-game outfielders last season? Better yet, did you roll that 4.33 P/G outfielder into your starting lineup on a regular basis last season? If you did, you were straddling the line of replacement level. Last week I placed offensive players into ranked tiers based on their final P/G achievements and 4.33 P/G is a tier-five player at best. Here’s a reminder of the points spread between tiers:

Offensive P/G tiers for all position players who played in more than 75 games in 2023:

Tier 1 range: 9.1 P/G – 6.0 P/G

Tier 2 range: 5.9 P/G – 5.5 P/G

Tier 3 range: 5.5 P/G – 5.1 P/G

Tier 4 range: 5.0 P/G – 4.7 P/G

Tier 5 range: 4.7 P/G – 4.3 P/G

Remember that represents all players clumped together. 4.33 P/G is actually pretty good if we’re looking at only catchers. The tiers above are independent of position and therefore, flawed. Today, I’ll dial in what should have been considered rosterable in 2023 by position, making note of what a replacement-level player recorded in points per game. Let this serve as a starting point as you may play in a different league format than I do, which would create different-sized player pools. You should be able to easily copy and paste the table in this article and edit the inputs accordingly. Before the table, I need to set the parameters:

– This is representing a 12-team, FanGraphs points league
– I am considering players on my bench above replacement level and am being somewhat arbitrary about it. Each league has 40 roster spots, but I’m leaving 10 of those roster spots for minor leaguers and below replacement-level players. If you add up the “Starters” and “Bench” columns, that is what I’m marking as each team’s number of above-replacement level players. Again, copy and paste the table and make edits if you wish.
– I have excluded players whose “Level” was anything but a major league team at the time of the data pull, eliminating minor leaguers.
– If a player is eligible for that position, they were included in the analysis for that position.

If you take all outfielders in your league, rostered or unrostered, and you sort them by points per game, you simply check the points per game mark of the 73rd-best player. But wait, isn’t a replacement-level player the player with the highest P/G mark available on the waiver wire? Well, yes and no. Let’s now put this system to the test with that 4.33 OF I mentioned in the intro. First, I’ll start by going into my league’s free-agent player pool, isolating outfielders who are currently free agents and played in more than 75 games last season. That last 75-game qualifier is not a part of the table above, but since I’m using end-of-season data, I want to show the players who accumulated playing time and kept a high points per game mark. Here’s what I see:

Andrew McCutchen – 5.24 P/G

Jeff McNeil – 4.35 P/G

Luis Rengifo – 4.34 P/G

Edward Olivares – 4.33 P/G

Willi Castro – 4.11 P/G

So, in theory, this mark works for my league. McCutchen, McNeil, and Rengifo were all hurt toward the season’s end, so in reality, the first available player eligible for the OF spot is Castro. To really prove this out, I’ll do the same exact thing in a second league. Here are OF eligible players available as free agents with over 75 games played:

Tommy Pham – 4.73 P/G

Harold Ramírez – 4.63 P/G

Jose Siri – 4.42 P/G

Ok, so it’s not perfect, but it’s close. I rostered Cedric Mullins all season and he finished the year at 4.37 P/G. Should I have dropped Mullins for Siri? Tough to say. Hindsight is 20/20. I still prefer Mullins for 2024. For now, this may help inform you of where you need to make cuts this offseason. Stay tuned for next week’s post where I work through this same exercise for points per game projections in 2024 and begin converting those projections into dollar values.

It’s important to know what a good points per-game mark is and what kind of price points are attached. In this post, I’ve taken all offensive players and isolated down to those who played in more than 75 games in 2023, an arbitrary cut-off. Then, I placed them in decile groups according to their points per game marks in 2023, creating tier groups. Each decile contains 32 or 33 players. I chose to only present the top six deciles, bringing the player totals to 196. That doesn’t necessarily cover a full league of rostered offensive players, but it gets close. Here’s a look at the spread of tiers one, two, and three:

Tier one shows us the largest spread due to outliers like Ronald Acuña Jr.‘s 9.11 P/G season, Corey Seager’s 8.31 P/G season, and the Dodgers combo Mookie Betts (8.14 P/G) and Freddie Freeman (8.1 P/G). Those were the four offensive players who held P/G marks above eight in 2023. The crazy part is that there’s no qualifier involved beyond the 75-game cutoff, meaning, the best players in 2023 stuck around and accumulated their way to the top. Each of these top of tier-one players played in at least 119 games. The spread of games across these top three tiers doesn’t differ much. However, the average price certainly does:

Average Games Played and Cost by Tier:
Tier 1: 138 G, $29
Tier 2: 141 G, $18
Tier 3: 129 G, $10

These prices are specific to my league but still give a good sense of what a top-tier player should cost. Once we get into Tier two, the price goes down and we’re left with very good everyday players who aren’t going 70-40, but are still contributing daily. Tier three is where I’d like to live. Here are max, min, and median players by P/G within each tier:

Let’s move on to tiers four, five, and six, where a tight four to five points per game range is the norm:

Now, we get down to the player pool where the really tough decisions get made. Does the player have more to give? Or, will they forever be a tier six player who you swap between bench and starting position? Are they likely to fall out of tier six and drop down into the dungeons of lower tiers? These are the questions that we’ll be asking ourselves all off-season as we click them back and forth between “cut” and “keep” on our “Roster Organizer” tab:

This analysis does not take into consideration the players who showed up for 75 games or less and crushed. Players like Davis Schneider, Royce Lewis, Evan Carter, and Zack Gelof are omitted from the box plots above. However, a big takeaway for me is how similar players in tiers four, five, and six are from a points-per-game standpoint.

Having a solid handle on what a good points per game mark is for full-season players is important and hopefully, it will give you some benchmarks for your offseason decisions. Next week, I’ll take a look at the distribution of position eligibility across tiers to get a sense of how valuable, for example, a tier one shortstop is over a tier four shortstop. See you then.

Yesterday, I reviewed a group of players THE BAT X forecasted for a significantly higher wOBA than Steamer did that I labeled as “sleepers” given their pre-season ADP. Today, let’s flip over to Steamer’s sleepers.

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Last week, I reviewed the wOBA forecast comparisons between THE BAT X and Steamer as part of the 2023 Projection Showdown. Let’s now stick with wOBA projections, but turn to the sleepers. For this showdown, I performed the same comparison as in the previous wOBA articles, but filtered only for hitters with an ADP of 300+, were signed with a team, and forecasted for at least 300 PAs. Let’s review the hitters who qualified as a sleeper that THE BAT X loved.

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Yesterday, I reviewed the results of the wOBA forecast showdown, filled with the hitters that THE BAT X was most bullish on compared to Steamer. Today, we’ll flip over to Steamer’s wOBA favorites.

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Let’s move on to batter wOBA forecasts as we review the results of the 2023 projection showdown, pitting THE BAT X against Steamer in various statistical categories. Today, we’ll begin by reviewing THE BAT X’s wOBA favorites, as compared to Steamer. While wOBA is typically not a fantasy category, it strongly correlates with home runs, RBI, runs scored, and batting average. And of course, keeps a hitter in the lineup if it’s high, while puts the hitter at risk of losing playing time if low. Let’s now find out how THE BAT X’s wOBA favorites performed.

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Yesterday, I reviewed THE BAT X’s runs scored favorites, as part of the 2023 projection showdown, pitting their projections against Steamer’s forecasts. Now let’s find out how Steamer’s runs scored favorites ended up performing.

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One of the very best aspects of Ottoneu fantasy baseball is that you are involved all year round. As October hits, stats are no longer accumulating, points are no longer being totaled and performance measurements remain locked in the past. But arbitration begins and the business side of baseball is front and center. Would you believe that someone, somewhere, in an Ottoneu points league is rostering Ronald Acuña Jr. for only $26? It’s true. If he’s not a candidate for arbitration allocation in that league, I don’t know who is. This is a pure example of why arbitration matters. If the person rostering Acuña for only $26 doesn’t have that salary adjusted, they would go into the draft with Acuña and the additional dollars to auction another big name. Oh, the humanity!

You can find all kinds of interesting situations like the Acuña one by downloading average salary data right from your league page and in this post, I’ll take you through the details on how you can use it to generate insights and strategy specifically for your league.

Step 1: Download average salaries and make it specific to your scoring format

If you click on the “Players” tab at the top of your league page, the sub-menu has a link all the way to the right titled, “Average Salaries”. You can also click here. The drop-down menu in the top right corner of the page allows you to specify salaries based on your scoring format. Choose the option that matches your league and then click “Export as .csv”.

Step 2: Merge average salary data with your league’s roster data.

If you followed Step 1 above, then you have your “average salary data”. Now, back on the “Players” tab, simply click “FanGraphs Sortable Stats”. This will take you to a FanGraphs leaderboard. Make sure the drop-down menu reads “All Teams” so that you are being given salary information for your league-mates rosters. Lastly, merge the two data sets on “Name” and subset it to “Player Name”, “Average Salary”, “Last 10”, and “$”, which represents the actual salary the player is currently rostered for in your league. Here’s an example:

You’ll notice a few things in the table above. First, I’ve also included “Last 10” which gives an average of the most recent 10 completed auctions for that player. You’ll also notice that I calculated the difference between the player’s average salary and what they are actually rostered for. From here I can start to make some decisions. For example, I’m not worried at all about the roughly $4.00 difference between the average salary and the actual salary for Benintendi and Kelenic. If you look at Benintendi’s “Last 10” you’ll notice he’s trending down anyways. By 2024, he may be worth less than the $5.00 he’s actually being paid. Yordan Alvarez, however, needs some adjusting. He’s worth more than $34, just try and change my mind.

Step 3: Group your new data set by team to determine who has the most “surplus value”

You can eyeball this process by simply going to your league’s “Arbitration” page and looking at the comparison of “Curr Salary” versus “Proj Salary” by team. Remember that you must give each team in your league at least $1. Note that this process does not provide a true “surplus” value for each team because each team is also overpaying on players. However, you won’t know who is being cut for a number of weeks, and that probably shouldn’t factor into your allocation strategy. It’s important to take stock of who in your league seems to have all the riches. There are a few different ways to do this but I like to take a simplistic route and isolate the league to players who are rostered lower than the average salary. Then, I sum the difference (Avg Salary – Actual Salary) by team and end up with something like this:

Right away I can see that there are two teams who have a big discrepancy between what they are paying and what other teams are paying on average. That could be a difference between two or three players, or it could be that a team is rostering a handful of players for a few dollars less than average. Let’s take a look at team F to see what is going on:

Team F looks a little less scary when you see that the value difference is spread out. Again, Yordan Alvarez needs to be adjusted. But, who else on this list should I allocate arbitration dollars to? Well, that’s the fun part. It’s not necessarily as easy as just tossing dollars on Yordan and Devers. Even if at the end of arbitration, Alvarez’s salary increases to $50, I’d probably still be inclined to keep him on my roster if I were the Team F manager. If that is the case, what does that actually do? Well, it limits the amount Team F will take into next year’s draft, but it doesn’t free up Alvarez for me to draft. That’s where the real strategy comes into play and over the next few weeks, our Ottoneu team will be writing more about arbitration strategy.

Today, we move on to reviewing the runs scored forecasts as part of the 2023 projection showdown pitting THE BAT X against Steamer in various fantasy categories. Just like RBI, runs scored is heavily driven by lineup spot. While the extra plate appearances for hitters atop the lineup doesn’t matter for this analysis because we’re keeping a constant 650 PAs, the guys at the top of the lineup benefit from better hitters behind them to drive them in. So they are likely to score a higher percentage of the time when they are on base versus hitters in the bottom half of the lineup. So let’s keep that in mind when reviewing the actual runs scored here as a change in lineup spot may be the cause of exceeding or missing the projections. We start with THE BAT X’s runs scored favorites.

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Tommy John surgery week continues with the hitter edition. With both Riley Greene and Jasson Domínguez (Shohei Ohtani got the brace procedure) getting a Tommy John surgery, I wanted to know how their performance changed from when they were healthy, to hurt, to fixed.

Note: I’m pushing my limits on what I’d like with a sample was 26 hitters. Sometime the matched seasons doesn’t lineup thereby pushing the number even further down. I understand if someone feels the sample is too small and blows off the results.

I found the change by using a weighted change from season to season. The hitters who had the most matched plate appearance got the most weight. Read the rest of this entry »