ChatGPT Guidance on projecting batting average

Default (GPT-3.5)

User
To project a MLB players batting average, what are the most predictive metrics to use in a formula?

ChatGPT
To project a Major League Baseball (MLB) player’s batting average, you can use a combination of both traditional and advanced statistics. While batting average is a relatively simple statistic, using more advanced metrics can provide a more accurate projection. Here are some key metrics to consider:

Historical Batting Average: Start with the player’s historical batting average data over the past few seasons. Recent performance is often a good indicator of future performance.

Plate Discipline Metrics:

Walk Rate (BB%): A player with a high walk rate can have a better understanding of the strike zone and may be more selective in their at-bats.
Strikeout Rate (K%): A lower strikeout rate indicates that a player makes more contact, which can positively impact their batting average.

Batted Ball Profile:

Batting Average on Balls in Play (BABIP): BABIP measures how often a batted ball becomes a hit when it’s put in play. A player with an unusually high or low BABIP may experience regression towards the mean.
Line Drive Rate (LD%): Line drives typically result in hits more often than other types of batted balls.
Ground Ball Rate (GB%) and Fly Ball Rate (FB%): A player’s batted ball profile can affect their batting average. For example, ground balls are more likely to result in hits than fly balls.

Quality of Contact Metrics:

Hard-Hit Rate: Measures the percentage of batted balls that are hit with a high exit velocity, which can lead to more hits.
Soft-Hit Rate: Conversely, a high soft-hit rate can indicate weaker contact and potentially lower batting average.

Pitch Recognition and Plate Coverage:

Swinging Strike Rate (SwStr%): A lower swinging strike rate suggests good pitch recognition and plate coverage.
Contact Rate (Contact%): A higher contact rate means the player is making more contact with the ball.
Splits and Matchup Data:

Platoon Splits: Consider how a player performs against left-handed and right-handed pitchers.
Performance Against Different Pitch Types: Some players excel against certain pitch types while struggling against others.
Ballpark Factors: Take into account the ballpark where the player will be playing. Some ballparks are hitter-friendly, while others are pitcher-friendly.

Age and Career Trajectory: Younger players may still be developing, and older players may be in decline. Consider a player’s age and career trajectory.

Injury History: Injuries can significantly impact a player’s performance and should be factored into projections.

Regression Analysis: Use statistical models that incorporate these metrics along with historical data to project future performance.

Remember that no projection system is perfect, and there is always an element of uncertainty in sports statistics. It’s important to continually update your projections throughout the season based on a player’s performance and any changes in their approach or circumstances. Additionally, context matters, so consider the specific player and situation when making projections.

User
Wow! that is a lot of metrics. Which of these are the five most predictive relative to the others?
ChatGPT
While all of the mentioned metrics can contribute to predicting a Major League Baseball (MLB) player’s batting average, some are generally more predictive than others. The importance of specific metrics can vary depending on the player and their unique characteristics, but here are five metrics that are often considered among the most predictive for batting average:

Historical Batting Average: A player’s recent history of batting average is usually the most predictive factor. Players tend to maintain a level of consistency in their performance, so their past batting averages are a strong indicator of what you can expect in the future.

Batting Average on Balls in Play (BABIP): BABIP is a key metric for evaluating how lucky or unlucky a player has been in terms of the outcomes of their batted balls. It’s often used to identify potential regression or progression in batting average. Players with abnormally high or low BABIPs are likely to see those numbers move closer to the league average over time.

Plate Discipline Metrics (Walk Rate and Strikeout Rate): A player’s walk rate and strikeout rate are good indicators of their ability to make contact and their understanding of the strike zone. A lower strikeout rate and a higher walk rate often correlate with a better batting average.

Batted Ball Profile (Line Drive Rate, Ground Ball Rate, Fly Ball Rate): The type of contact a player makes can significantly impact their batting average. Line drives tend to result in hits more often than other types of batted balls, so a high line drive rate is predictive of a higher batting average.

Hard-Hit Rate: Hard-hit rate measures the percentage of batted balls hit with high exit velocity. Players who consistently make hard contact are more likely to get hits, making this metric a strong predictor of batting average.

These five metrics provide a solid foundation for projecting batting average. However, it’s essential to consider these metrics in combination, as no single metric can provide a complete picture of a player’s batting ability. Additionally, contextual factors, such as a player’s age, ballpark, and matchups, should also be taken into account when making predictions. Baseball is a complex sport, and multiple factors can influence a player’s performance.

ET

Thanks! I would like to rank my player projections +/- from the batting average for the top 400 players in plate appearances. I would like 1.00 to represent a dead average projected batting average, and better player projections to achiever a score such as 1.20. Vice versa for the worst projections, e.g., .85, for example. Can you provide an excel formula with the top five metrics you just provided for my spreadsheet?

ChatGPT

Certainly, you can create a scoring system in Excel to rank player projections relative to the league average batting average, with 1.00 representing an average projection. You can assign weights to each of the five metrics you mentioned and calculate an overall score for each player. Here’s an example of how you can set up this scoring system in Excel:

Assuming your player projections for the top 400 players are in columns B to F (one column for each metric) and that your league average batting average is in cell G1:

1. First, calculate the z-score for each metric. The z-score measures how many standard deviations a player’s performance is from the mean.In cell H2 (for the z-score of Historical Batting Average), enter the formula:swiftCopy code=(B2-G$1)/STDEV.P($B$2:$B$401) Drag this formula down to calculate z-scores for all players for this metric. Repeat this process for each of the five metrics, placing the z-scores in columns H to L.

2. Next, assign weights to each metric. Let’s assume you want to assign equal weights to all five metrics.In cell M1 (for Weighted Score), enter:cssCopy code=(H1+I1+J1+K1+L1)/5 This formula calculates the average z-score across all five metrics.

3. Finally, calculate the overall score for each player relative to the league average batting average. Assuming you want 1.00 to represent an average projection, enter the following formula in cell N2 (for Overall Score):swiftCopy code=1+((M2-AVERAGE($M$2:$M$401))/STDEV.P($M$2:$M$401))

This formula calculates an overall score for each player, with 1.00 representing the league average. Drag this formula down to calculate scores for all players.

Your spreadsheet should now have an “Overall Score” in column N that represents how each player’s projected batting average compares to the league average, with 1.00 as the average projection. Players with scores above 1.00 are projected to perform better than average, and those below 1.00 are projected to perform worse than average. Adjust the weights for each metric in the “Weighted Score” calculation if you want to give certain metrics more or less importance in your projections.

User
OK. Based on the predictive quality of each metric, how would you recommend I weight each of the five?
ChatGPT

The optimal weightings for each of the five metrics can vary depending on the specific data and the context of your player projections. However, you can consider a balanced approach based on the general predictive quality of each metric. Here’s a suggestion for assigning weights to the five metrics:

Historical Batting Average: This is often the most reliable predictor, so you might assign it a higher weight. A weight of 30% is a reasonable starting point.

Batting Average on Balls in Play (BABIP): BABIP is a valuable metric for assessing luck and regression potential. Assign it a moderate weight, such as 20%.

Plate Discipline Metrics (Walk Rate and Strikeout Rate): Plate discipline metrics can be quite indicative of a player’s ability to make contact and select pitches. Give them a combined weight of 20%.

Batted Ball Profile (Line Drive Rate, Ground Ball Rate, Fly Ball Rate): The type of batted balls a player generates can influence their batting average. Assign a weight of 15% to this category.

Hard-Hit Rate: This metric measures the quality of contact and is also significant. Give it a weight of 15%.

So, your weighted score formula in cell M1 would look like this:

scss
Copy code
=(H1 * 0.30 + I1 * 0.20 + (J1 + K1) * 0.20 + L1 * 0.15) + (M2 * 0.15)

This assigns higher weights to the more predictive metrics like historical batting average, BABIP, and plate discipline while still considering the influence of batted ball profile and hard-hit rate. These weights provide a balanced approach, but you can adjust them based on your specific data and the relative importance you believe each metric holds for your player projections.

User
Thank you, ChatGPT!

Using ChatGPT to craft HR projections for the 2024 MLB season

The MLB season will be over in a week or so. That means the 2024 season is just around the corner!

I am using ChatGPT for some insight into my home-made projections while I await Steamer. Here’s what she says about projecting a player’s HR total in response to the following query:

ME: In MLB fantasy baseball, what are the best predictive metrics like barrel rate to predict a player’s homerun total the following season?

In MLB fantasy baseball, predicting a player’s home run total for the following season can be challenging, but there are several metrics and factors you can consider to make more informed projections. While barrel rate is a valuable metric, it’s important to combine it with other statistics and factors to create a well-rounded projection. Here are some key metrics and considerations to help you predict a player’s home run total:

Barrel Rate: Barrel rate measures how often a player makes ideal contact with the ball, resulting in the best combination of exit velocity and launch angle for home runs. A high barrel rate indicates a player’s ability to consistently hit the ball hard and at an optimal angle. This metric can be a good indicator of power potential.

Exit Velocity: Exit velocity measures how fast the ball comes off a player’s bat. Players with consistently high exit velocities are more likely to hit home runs because they generate more power. Statcast data provides exit velocity information.

Launch Angle: A player’s launch angle, or the angle at which the ball leaves the bat, can significantly impact their home run production. Players who consistently hit the ball at launch angles that optimize power are more likely to hit home runs.

Historical Performance: Review a player’s past seasons to see if they have a history of hitting for power. Look at their home run totals over the last few seasons and their career as a whole. Consistency in power production is often a good sign.

Plate Discipline: A player’s plate discipline can also affect their home run production. A batter who has a good eye and swings at pitches within the strike zone is more likely to make solid contact. Metrics like O-Swing% (percentage of pitches swung at outside the strike zone) and Z-Swing% (percentage of pitches swung at inside the strike zone) can be informative.

Ballpark Factors: Consider the ballpark in which the player will be playing. Some ballparks are more hitter-friendly and can boost a player’s home run numbers.

Age and Health: Younger players may continue to develop power, while older players may see declines. Additionally, a player’s health and injury history can impact their ability to hit for power.

Team and Lineup Context: The quality of a player’s supporting cast and their spot in the lineup can affect their RBI and run-scoring opportunities, which can indirectly impact home run production.

Offseason Changes: Pay attention to any changes in a player’s offseason training regimen, swing adjustments, or approach changes. These factors can influence power production.

Statistical Models: Some fantasy baseball analysts use statistical models and projections to estimate a player’s future performance. These models often incorporate various metrics and historical data to make predictions.

Keep in mind that while these metrics and factors can be helpful, there are no guarantees in fantasy baseball. Players can experience unexpected fluctuations in performance due to various factors, including luck and variance. It’s essential to use these metrics as part of a comprehensive analysis and not rely solely on one statistic to make your predictions.

These are some good suggestions. Let’s see what she says about an Excel formula to generate a useful formula to use with projected plate appearances. Here’s my query:

Those were some good suggestions for my MLB projections. Now I need to formulate an Excel formula to crunch some of those metrics. Can you suggest an excel formula to use the following metrics: barrel rate; launch angle; exit velocity; and plate discipline. Also, please add the metric OPS to the list. I would like to generate a number that is + or – 1.00. In my example, 1.00 is the average number of homeruns for MLB players who had at least 400 Plate Appearances.

To create an Excel formula that calculates a projected home run number based on the metrics you mentioned (barrel rate, launch angle, exit velocity, plate discipline, and OPS), you can use a weighted average approach. You’ll assign weights to each metric based on their importance and then sum up the weighted values to generate a projection. Here’s a formula that you can use as a starting point:

Excel=(((BarrelRate * WeightBarrelRate) + (LaunchAngle * WeightLaunchAngle) + (ExitVelocity * WeightExitVelocity) + (PlateDiscipline * WeightPlateDiscipline) + (OPS * WeightOPS)) – AverageHR) / StandardDeviationHR

In this formula:

BarrelRate, LaunchAngle, ExitVelocity, PlateDiscipline, and OPS should be replaced with the actual values for each player.
WeightBarrelRate, WeightLaunchAngle, WeightExitVelocity, WeightPlateDiscipline, and WeightOPS are weights you can assign to each metric. These weights should add up to 1. You can adjust them to emphasize the importance of each metric in your projections.
AverageHR should be replaced with the average number of home runs for MLB players who had at least 400 Plate Appearances.
StandardDeviationHR can be the standard deviation of home runs for the same group of players. This helps you scale the result to a range of approximately +1.00 to -1.00.
Here’s a step-by-step guide on how to use this formula:

Calculate the average and standard deviation of home runs for MLB players with at least 400 Plate Appearances.
Assign appropriate weights to each metric based on their importance.
Replace the placeholders in the formula with the actual values for each player and the calculated average and standard deviation.
The result will give you a projected home run value for each player, which you can use for your fantasy baseball projections.
Please note that this formula is a basic starting point and may need further refinement based on your specific analysis and data. Additionally, consider normalizing the metric values if they are on different scales to ensure that no single metric dominates the projection.

This is great. Based on your knowledge of these various metrics and their historical predict value, what weights would you assign to each metric?

Assigning weights to each metric depends on your analysis and how much you believe each metric contributes to a player’s home run production. It’s essential to consider historical predictiveness, but also keep in mind that the effectiveness of these metrics can vary from season to season and from player to player. Here’s a general guideline for assigning weights based on their historical predictiveness:

Barrel Rate: Barrel rate is a strong indicator of a player’s ability to make solid contact, and it tends to correlate well with power production. Assigning a relatively high weight to barrel rate, such as 0.3 or 0.4, can be a good starting point.

Launch Angle: Launch angle is crucial for determining the trajectory of the ball. Assign a moderate weight, such as 0.2 or 0.3, as it can significantly impact home run production.

Exit Velocity: Exit velocity measures the speed of the ball off the bat and is an important component of power. Assign a weight similar to launch angle, such as 0.2 or 0.3.

Plate Discipline: Plate discipline metrics like O-Swing% and Z-Swing% indirectly impact power production by influencing a player’s ability to make solid contact. Assign a lower weight, perhaps 0.1 or 0.2, as plate discipline alone may not be as predictive of home runs.

OPS: OPS (On-base Plus Slugging) is a well-rounded metric that includes both on-base percentage and slugging percentage. It captures a player’s ability to get on base and hit for power. Assign a moderate weight, such as 0.2 or 0.3, to OPS.

Remember that these weights are just a starting point, and you may need to adjust them based on your analysis, data, and the specific context of the season. It’s also a good idea to track the actual performance of the players you project to see how well your weights and formula are working, and then make adjustments accordingly. Over time, you can refine your weights to improve the accuracy of your projections.

Author: ChatGPT Title: “Predicting MLB Fantasy Baseball Home Run Totals: Key Metrics and Factors”

Can a reshuffled Tar Heels O-line protect star QB Maye from the Gamecocks?

The Tar Heel sports media is busy hyping up reigning ACC Player of the Year, Drake Maye, the quarterback. He was an early 2022 dark horse to win the Heisman Trophy. Now again in 2023, the Heisman hype for Maye is already taking off. See these two example articles by @RoddBaxley Fayetteville Observer and @RichardJAdkins Tarheels Wire. Undoubtedly, there’s more to come. However, Mr. Maye’s 2023 Heisman campaign will go off the rails quickly if a newly constituted O-line cannot protect him.

As the season opener approaches (in just four days!), questions about The Tar Heels O-line remain a focus for the Tar Heel sports media. As a Gamecock, this was a curiosity. The Tar Heels return four of their five starters from last season. I dug into it to unravel the mystery.

The primary bone of contention is finding a good fit at left tackle. It appears that 320-pound redshirt senior William Barnes is the man to protect Maye’s blind side. Barnes has played in 52 career college games, and made 13 starts last season as a right guard. So, what’s the problem?

Well, his pre-season position transition to left tackle has been bumpy. Early in pre-season camp, after lamenting the sacks of Tar Heel QBs in a scrimmage, Tar Heel football coach Mack Brown said “the tackles have been inconsistent.” Mack Brown emphasizes the need to avoid sacks on offense

That triggered the Tar Heel sports media. They went into attack mode. Putting the redshirt senior player under intense grilling, Barnes conceded that he’s playing left tackle for the first time since his high school junior year. (That begs the question, was he even good enough as a high school senior to play tackle?) After reporters repeatedly asked pointed questions about the transition from guard to tackle, Barnes finally acknowledged. “It’s definitely been challenging. Moving from right guard to left tackle–the transitioning–it can be difficult.” He admitted bumbling up his footwork and being confused by offensive line play calls. Nevertheless, Barnes attributed the sack issue in the scrimmage to the excellent Tar Heel defensive line personnel and claimed that with coaching help he overcame these earlier difficulties. https://www.youtube.com/watch?v=T9mr2SJArg0

The Tar Heel media turned their attention on the new O-line coach, Randy Clements. Reporters interrogated Clements about the perceived O-line sack issue. One reporter asked Clements if the O-line was “soft” when he arrived. Clements deferred criticizing his predecessor but acknowledged everyone has “different styles.” He then contrasted one philosophy of an O-line playing for stalemates verses another philosophy (his!), “playing to dent the line of scrimmage and bloody somebody’s nose.” https://www.youtube.com/watch?v=hVHEM15Tv0s (7:45)

Under interrogation, Clements portrayed confidence in Barnes and assured the Tar Heel media Barnes would produce at left tackle. Clements rated Barnes as an A+ left tackle. He cited Barnes coachability, work ethic, spirit of competitiveness, and humility. https://www.youtube.com/watch?v=hVHEM15Tv0s (4:20) Clements also noted that Maye, being a very athletic QB, is able to escape pass rushers with his scrambling style.

That was not so much the case, however, as UNC wound down the 2022 season with a string of losses. In those games, Maye’s QB rating declined as better teams focused on containing his scrambling threat. Those teams kept Maye inside the pocket. In those games, an issue of Maye’s pocket presence or happy feet emerged as the avenues for escaping the pocket were cut off by good defenses.

An early take-a-way for the Gamecocks opener is for us to watch and see if the Gamecocks contain Maye in the pocket while (hopefully) collapsing it on him. The Gamecocks defensive linemen cannot let Maye scramble out and threaten their defense by extending plays and with his excellent running ability. And if they do meet this goal of keeping Maye in the pocket, watch and see if Maye has improved his pocket footwork, or if like last season, he becomes a nervous nelly. If Maye has improved his pocket presence during the off season, he will be an even more dangerous QB than he was in 2022. Adding that skill to his QB toolbox will certainly improve Maye’s odds of winning a Heisman Trophy for himself.

Online Auction Championship Strategy @NFBC: Team #1391

Here is a color-coded roster on the Saturday night Online Auction Championship team:

Starters and power hitters are in pink. Closers are green. Power speed combo players are blended. Pure speed players are green.

Online Auction Championship Strategy @NFBC: Team #1347

Here is a color-coded roster on the Friday night Online Auction Championship team:

Starters and power hitters are in pink. Closers are green. Power speed combo players are blended.

More About the @NFBC Online Auction Championship

As noted in the last post, I competed this weekend in the NFBC Online Auction Championship contest. For this fantasy baseball rotisserie draft, each competitor in a fifteen-team league has $260 to spend on acquiring 23 MLB players. I enjoyed myself so much on Friday, that I decided to compete again in the contest on Saturday. Thus, I undertook two auctions this weekend on Friday and Saturday night. This post compares my money-spending strategies.

The Friday night auction was the first auction in which I have participated since 2019. 2019 was my first year playing fantasy baseball at the NFBC. At that time, I was beginning my journey in learning how to compete in these contests. In 2019, I did not know the MLB and MiLB player pools well enough. My auction teams in 2019 were uncompetitive.

I have spent a lot of time since then learning how to be a better competitor. There is a lot to learn if you wish to compete in high stakes fantasy baseball. This year, I started prepping for the 2023 NFBC contests in November.  I have a good notion of the player pool and I think I know the players who will perform well in 2023. Compared to 2019, I am far more prepared for one of these auction contests.

The Friday night auction competitors in my league from top to bottom were less experienced in the NFBC. The competition on Saturday night, however, was filled to the brim with NFBC stars, including Zachary Waxman, who won an auction championship in 2021, and Lindy Hinkelman, an NFBC legend and one of the top earners in the NFBC. There were other highly reputable characters in the Saturday night auction.

My spending strategy for each auction varied. I was far less aggressive in my early bidding on the Friday night auction. On Saturday night, however, I was more aggressive and acquired a larger percentage of my players in the early third of the draft. The two pie graphs below depict the percentage of money spent in each phase of each auction.

For what it’s worth, fantasy, pros software, graded both of my drafts as a B-. I don’t use the fantasy pros draft guide, so this is unsurprising.

One takeaway. I mostly did have to pay up for my guys. The fact good players were bidding the price up on the players i am targeting gives me hope that I’m on the right guys for 2023. I overspent by $10 on Friday to get my guys. I overspent by $8 on Saturday. I will post more here about the players on each team soon.

My Experience with an @NFBC Online Auction Contest

I undertook an NFBC online auction championship contest last night. This post recounts my experience.

The entry fee is $150 and the contest is a fifteen team roto with typical NFBC scoring and a free agent acquisition budget. There is a $10,000 overall prize. League winners collect $900. One needs to finish in the top three slots of the league to cash.

First, the online auction software at NFBC is fabulous. My impression of the overall experience was extremely satisfied. Your display shows the average auction value of every player nominated. It shows all of your opponents and the amount of budget they have left to spend. It is also simple to navigate from the draft room to an “Auction Board.” The Board graphically shows what positions have been drafted for all players in the contest as well as the budget data for each.

A contestant can enter a draft room in advance of the auction. This allows one to prepare for the auction by entering maximum bids for targeted players. As is explained below, one must be vigilant when using this software.

Now, turning to my auction execution. The auctions phase moved extremely rapidly. If you plan on using any draft software to help you evaluate pricing as the auction moves forward, then I recommend you have at least three monitors up and running during the auction. On one monitor, you would need the draft room. On the second, you need the auction board. On the third, your Excel spreadsheet or other draft software.

I executed this auction using only one monitor. Therefore, I was very limited in outside resources and had to rely on the draft room to play. I was able to occasionally flip back-and-forth between the auction board in the draft room. However, doing so was risky because of the speed the auction was moving.

I did enter a lot of automatic (max) bids on around 30 targeted players at the outset. However, if you aren’t on top of it, the software will get you in trouble quickly by spending your budget in the early phases by buying you too many star players. Therefore, in my draft, as soon as I acquired Yordan Alvarez for $35, I deleted all the other auto (max) bids for batters above 25.

I did the same for pitching. I nabbed Gerrit Cole for $35 and Josh Hader for $25, I then quickly deleted the auto bids or scaled them back so they were well below the AAV. This made it less likely I would bust my budget and wreck my auction strategy within the first 20 minutes. I do believe you need to acquire a couple of stars in an auction. However, if you buy too many star players, you will bust your budget of $260 and not be able to acquire any middle rounds players. This will create an overall weak team with a couple star players.

I was unable to access my Tanner Bell Excel spreadsheet. Therefore, I had to rely on my memory. I do have a satisfactory knowledge of the 2023 player pool for this year. Therefore I felt I was able to manage in a less than optimal situation.

The auction itself lasted approximately three hours. Contestants were afforded two five-minute breaks after the seventh and 15th rounds. The auction portion of the draft ended after round 23. We were then provided another five minute break. At that point the draft morphed into a seven-round snake draft for the final reserve picks. This reserve round snake draft also went extremely fast with one minute per person to make picks. At least it seemed fast to me as I have only done slow drafts so far in 2023.

At the morphing point, I was able download the draft results and plug them into my Tanner bell spreadsheet. I executed this download two or three times during the reserve rounds. This was critical, to help me identify targets leftover after the auction. I was able to get guys like Aaron Civale, Andrew McCutchen, Aledmys Diaz, Michael Massey, and three other lesser late round targets.

My @TGFBI Draft Strategy. Will it work?

All thirty rounds of my #TGFBI draft are finished. I blogged about the first ten rounds of this draft in this post. The FantasyPros gave my team a B- and projected a 9th place finish. FantasyPros graded my team weak in runs, average, and ERA.

This post relates my overall thoughts about my draft strategy and plan, and how I believe it worked. My first goal of this draft, as has been my goal of all my drafts this season, was to take the gifts provided by the draft. In doing so, I also wanted to draft a balanced team that would compete for the overall prize. Finally, I drafted an extremely flexible group of offensive players many of whom are eligible at several positions.

Offense

Categorically, the team is balanced. I have good power from my early hitters and catchers, and I also got some decent speed assets as the draft played out.

Positionally, the roster is weakest at shortstop and outfield. I did take a later round shortstop whose upside I like quite a bit, Oswald Peraza.

Peraza, a Yankees rookie shortstop, is still in a competition for the starting job as I write this. However, the experts believe Peraza is a more polished defensive shortstop. Therefore, because I know defense is super important in real baseball (while no so much in fantasy baseball) I remain hopeful Peraza will snag the everyday job for the Yankees. If he does, he has speed and could steal 15 bags.

My first backup plan at shortstop was Ha-seong Kim. Kim is projected as the starting Padres 2B for 2023. The projections are not high on Kim, but I think he has a chance to exceed them. I like the Padres offense even if it is likely Kim hits toward the bottom of the Padres order.

I also have two other sneaky shortstops on this roster, Chris Taylor (Dodgers) and Aledmys Diaz (A’s). Neither are qualified for SS presently, but should be soon. Those two players gives me additional depth in the worst case case where Peraza is sent to the minors.

As far as outfielders, the only big name here that I drafted was Yordan Alvarez. He has a sore hand now but I’m hopeful that injury will not take much of his playing time. Alvarez is a key part of this offense. He hits for tremendous average and power and is in the heart of the Astros lineup. The rest of my outfielders from OF2 to OF9 are interchangeable. Several of them play middle infield or first base so I can move them around my lineup depending on the Razzball bi-weekly projections. The lineup will be optimized each week.

I also have Jazz Chisolm, who is learning to play centerfield for the Marlins, and the Dodger rookie Miguel Vargas. Chisolm should qualify for OF within a few weeks of the season. Vargas is expected to primarily play second base (now qualified at 1B) and will soon qualify at 2B. This will provide even more flexibility on this team.

Pitching

Even though I waited until round 5 to take any starting pitchers, I am happy with the way this staff turned out. Of course, most everyone feels good about their team right after a draft.

I do have what I project as three primary closers on this roster in Hader (Padres), Lange (Tigers), and Hudson (Dodgers). Hudson has a leg injury, but he is throwing now and is expected to be in the Dodger bullpen by mid April.

The top five starters on this team are all solid. I expect to stream one or two starters (out of the remaining four) off the bench depending on weekly matchups.

Conclusion

I believe this team will outperform the FantasyPros assessment. The flexibility built into this team should allow it to maximize statistics all season. I am hopeful that it will not only grind its way to a win in this individual league, but compete for an overall TGFBI championship.

The Great Fantasy Baseball Invitational @TGFBI

This is my third year competing in TGFBI. I’ve been assigned to a league of fantasy assassins for 2023, League 5. Another challenge. Go to the link and scroll down for details.

Looking Back to 2022: What Went Wrong?

In 2022, I finished 77th overall, which was in the top 17 percent. I was 4th in my league.. I attribute my lack of 2022 TGFBI excellence to the strong set of competitors in my league, and a bad draft during which I made some poor picks. Particularly, my picks at the rounds 8/9 turn were horrendous. I rationalized it in real time. Looking back, those rounds 8/9 selections were panic picks. I felt I was being shut out of 3B and massively over-reacted.

The good part of making mistakes is you get to learn from them. I understand better my propensity for panicky picking, and now guard against it. Whenever I hear that voice telling me, “you’re missing out on 3B [or whatever],” I answer back: “TGFBI 2022!”

The fear I felt in 2022’s TGFBI derived from being overly concerned about tiers. Tier drafting is a thing in fantasy baseball. Players try to grab the best bargains at each tier. The problem with that style of drafting, for me, is that it leads to panic. That is where you make gigantic mistakes in the draft room. You tell yourself you must reach for certain guys or you will miss out on the tier. I’m not saying a good tier drafter cannot make the strategy work. Just that for me, it doesn’t work as well.

League 5 2023 Draft

Here is a link to our League 5 Draft Board . My picks through the first 10 rounds are shown in the feature image of this post.

I am drafting from the 7 hole, which I like because I feel like I am dictating the room rather than reacting to it. (Last season, I was drafting from the turn. ) For this draft, I didn’t take an SP1 until round 5, selecting the Guardian’s Shane Bieber with pick 67. Since, I’ve stocked up on solid starting pitching taking 5 SPs with my first ten picks. I don’t have a true ace on this TGFBI team pitching staff, but maybe I can make up for it because my SP4 and SP5 will be stronger than my competitors’ SP4s and SP5s.

Why so pitcher heavy this year? Several reasons. First, I like a lot of the offensive players in the back of the 2023 player pool. Second, you can get offense and saves off the waiver wire during the season. Finding good reliable starters on the waiver wire during the season is more difficult.

Should I trust @EnoSarris ‘s Pitching+ Model? @ven_armbarn @PileOfDial @InTheCutFantasy

There is a lot of buzz this year about Eno’s new Stuff+ pitching model. You can find the tables (sort of) by subscribing to The Athletic and finding Eno’s author page. Scrolling around a bit will lead you to a few recent articles with pitching rankings. Based on my anecdotal observations, it seems like a lot of fantasy competitors are using that model for 2023 drafts to evaluate pitchers.

I get it. Everyone wants to find an edge. And a model that measures movement of individual pitches, and other granular data, to mathematically find pitchers likely to outperform ADP and/or projections, gives you an edge. I love Eno’s work. A journalist, he is talking to MLB pitchers about their craft. I’ve been following Eno on Twitter for years. Bringing his inside knowledge of pitching into a mathematical model, well that sounds like a great endeavor for fantasy baseball geeks like myself.

With all this inside information, I checked to see how Eno performed in the 2022 @NFBC Draft Champions contests

Eno entered three DC contests last year and his teams were ranked as follows: 315, 2,342, 2,872, Not bad.

One of the hottest and best fantasy baseball podcasts to emerge this year is In The Cut with John Fish and Ryan Venancio. I’ve listened to all of the episodes. Ryan is heavily reliant upon Eno’s Stuff rankings. Ryan drills deep into pitch mixes and induvial pitch performance. It’s great. You shouldn’t miss the fantasy commentary. Ryan has confidence, and strong opinions on starting pitchers. Many of them seem to derive from Eno’s work.

Based on this, I assumed Ryan relied upon Eno’s Stuff+ for his 2022 DC season drafts. [UPDATE: the assumption was wrong. Ryan tweeted as follows: “I only started using Eno[‘]s pitching metrics at the very end of draft season last season….”] Ryan said 2022 was his first competing in the NFBC. He performed better than I did in my first NFBC year. In 2022, Ryan entered 8 DC contests. Only his last DC drafts are relevant to evaluate Eno’s rankings. His DC overall teams were ranked 550, 958, 1,555 (#272), 1,960, 2,528, 2,958 (#352), 3,601, 4,223. (There were 4,815 teams in the contest.) I bolded Ryan’s last two DCs. The bolded contests were the contests he may have used Eno’s 2022 pre-season pitching rankings. Probably, this sample is too small to draw any conclusions about Eno’s projections. However, Ryan said he will be using them for his 2023 draft process so we will be a good test for next year and Ryan is paying the price for us!

By contrast, I didn’t rely on Eno’s model at all for my 2022 DC drafts. My process was more focused on K%, BB%, and SIERA. I also looked at SSW data on individual pitch basis, velo, etc. I spend a lot of my time analyzing team context as well. I use my process to differentiate my targets from other pitchers around the fluctuating market ADP .

I entered 9 DCs last year with decent results: 489, 490, 686, 761, 986, 1,308, 2,218, 2,320, and 4,296. My performance last year was almost entirely successful, to the extent it was, based on my pitching SGP. Based on this, I think I’ll stick to my pitching process this year. My goal this year is to improve my hitting SGP. If I do that, then I think I can cash more often than last season.

I also want to see how Eno’s new Stuff+ pitcher rankings works out for 2023 before I go all in betting on it.

That said, some of my 2023 targets are on Eno’s list. Based on 2023 fantasy group think and the trends I am seeing now, I think Eno’s Stuff+ pitchers will move up in ADP. Therefore, I have identified my targets that are coincidentally on Eno’s list, I am bumping them a round to avoid snipers.