Showing posts with label Value. Show all posts
Showing posts with label Value. Show all posts

30 July 2018

The Value Of #4/#5 Starters Has Skyrocketed

Most fans hope for the best for their team. They hope that their major league players will show improvement from their past performance and that their top prospects (regardless of overall rank) will end up being successful in the majors. This divide between optimism and reality becomes clearer when looking at starting pitching. People hope that their top pitching prospects can become successful in the majors at the same time that starting pitching is becoming hard to find. As a result, fans undervalue legitimate backend starters and overvalue unranked pitching prospects. This came to light last week when Jon talked about the value of Kevin Gausman.

For starters, the performance of starting pitching has changed significantly recently. This chart shows the count of qualified pitchers, their average ERA (not waited by innings pitched), their average FIP and their average WAR.



It’s pretty simple, there are 30 teams in the majors and each team historically has five starters in the rotation, meaning there are 150 starters that have a shot to be qualified. From 2010-2014, roughly 90 starters threw over 160 innings, or on average each team had three qualified starters. In 2017 that dropped to 56 starters, or on average each team had only two qualified starters. However, despite the drop in qualified starting pitchers, their performance hasn’t improved. The average ERA and FIP have gotten worse over time, suggesting that finding qualified pitchers is harder in this new age. That’s one reason why only 25% of qualified starters were worth 2 WAR or less in 2017. Starters that can give their team 160 innings with a decent ERA and FIP have become much more valuable than they were even two years ago.

Unsurprisingly, the number of starters used in a year has gone from 273 in 2010 to 315 in 2017. Part of this is because teams received on average 970 innings from their starters in 2010 but only 890 innings from their starters in 2017. But part of it is that the average starter has gone from throwing 106 innings in 2010 to only 85 in 2017. As a result, teams have gone from using 9 starters on average to using 10.5 starters on average. As more starters are used, the average ERA and FIP has also gotten worse. Things are somewhat better this year, but not by much. According to TruMedia, there are 79 qualified starters in 2018 compared to 73 at this point in 2017. Expect a small increase of qualified pitchers from last year, but probably not a large one. Here's how the numbers look for all starters.



The value of backend starters that can give you a large amount of innings without having terrible results has skyrocketed due to their scarcity. Teams only have so much starting pitching depth. The more starters that they’re forced to use, the more likely that they’re going to get an atrocious performance from somebody. The teams with the best starting pitching are those like the Indians who made it through 2017 using just seven starting pitchers. Having guys like Bundy and Gausman on your roster helps keep the bullpen fresh and ensure teams don’t need to use their AAAA guys as starters.

It’s possible to use FIP to rank starting pitchers from 2010-2017. For each year, we know how many starters each team used on average, so it makes sense to put starters in groups based on the average number of starters used by a team. For example, in 2010, teams used 9.1 starters per year, so we can rank starters from 1 (best) to 10 (worst) based on their FIP. In 2017, teams used 10.5 starters per year, so we can rank starters from 1 (best) to 11 (worst).

When using this method, it becomes pretty clear that there’s a big difference between aces (average FIP of 2.59) and #2 starters (average FIP of 3.28), the second worst starters (average FIP of 5.27) and the worst starters (average FIP of 8.12) and the third worst starters (average FIP of 4.72) and the second worst starters. Aside from those groups there’s roughly a .2 or .3 FIP difference between ranks. Over 180 innings, this is equivalent to roughly 5 runs or half a win. A decent-sized distinction, but not a huge one. Here's how the groupings look.



Using this method, Kevin Gausman consistently (2015-2017) appears to be a #5-6 starter while Dylan Bundy looks to be an outright #6 or even worse. That stated, FIP probably isn’t particularly fair to Orioles starters. FIP presumes that pitchers are fully responsible for all home runs that they allow, but it’s a lot easier to hit a home run in Camden Yards than in the Oakland Coliseum. Fangraphs WAR uses a park factor to take this into account, but FIP does not. So, it probably makes sense to consider Gausman a #4-5 starter. Likewise, Sonny Gray was a 3-4 starter in 2014 and 2015 using this metric, while he dropped to a #8 starter in 2016. But due to pitching in a pitcher friendly stadium, it’s likely he should also have been treated as a #4-5 starter. In other words, these two pitchers are probably closer in value than just looking at their FIPs or ERAs would indicate.

At any given time, there are typically around 40 pitching prospects on top 100 prospect lists. Not all of these pitchers graduate in a given year, but if top prospects had a high success rate, then there would be a lot more than 60 qualified starters. The fact is that the likelihood of a top prospect being successful isn’t great, and therefore the value of a prospect that is successful is high. If top pitching prospects that are ranked struggle to be successful, then pitching prospects that aren’t ranked struggle even more often. It’s highly unlikely that unranked pitching prospects will be successful in the majors. They’ll get a shot because teams need to rely on their minor league system for starters, but they’re probably not going to succeed. Unfortunately, fans don’t remember failed prospects.

Kevin Gausman and Dylan Bundy are likely going to be nothing more than #4-5 starters on the Orioles. It’s possible that another team could successfully develop them and turn them into top of the rotation pitchers. But even #4-5 starters that can pitch a full season have significant value. Their performance may not be great, but these guys can solidify a rotation, ensure that teams don’t need to rely on minor league pitchers with minimal talent and preserve a bullpen. The value of that has skyrocketed over the past few years.

24 December 2014

Team-Controlled Players Have Seen a Pay Cut

I understand that sometimes when I write sabermetric posts that they can be hard to follow. What I'm going to try to do in the future is write a paragraph or two discussing the relevant points at the start of the article.  I'm hoping that doing this will make them easier to follow and make clear what I think are some of the most important points to note.

Abstract: In this post, I intend to show that both the cost of a free agent and team-controlled win (defined as a player with fewer than six years of service time) have increased from 1996 to 2013. The cost of a free agent win is increasing more than 3.9% annually more than a prospect win from 2004 to 2013. This indicates that team-controlled players are being underpaid compared to free agents. It also means that any attempt to determine a discount value for keeping prospects in the minors needs to consider that the value of a prospect win increases over time by 3.9%.

A few months ago, Lew Pollis wrote an article discussing the historical cost of a win in free agency from 1996 to 2013. He determined how much money teams spent on free agents in a given year (regardless of whether they were signed that year or not), used Fangraphs WAR to determine their production, and then divided the two. It is possible to use his method to do the same thing with team-controlled players and see whether their value has changed over that 17-year time frame. This makes it possible to determine whether a team-controlled win has increased in value compared to a free agent win and therefore if prospects are becoming more valuable over time.

The amount of money spent on payrolls has increased significantly from 1996 to 2013. Teams spent $984 million on payroll in 1996 and $3.138 billion on payroll in 2013. Team-controlled players (players with less than six years of service time) earned $357 million in 1996 and $1.323 billion in 2013. Extended players (players with more than six years of service time but did not sign in free agency) earned $420 million in 1996 and just $504 million in 2013 while free agents (players that have more than six years of service time and were signed in free agency) earned $207 million in 1996 and $1.31 billion in 2013. The chart below shows how much money free agents, team-controlled players, and extended players earned from 1996-2013.

The amount of production that each group produces partly explains this trend. Free agents have produced roughly 200 WAR per season from 1996 to 2013. However, extended players produced 324 wins in 1996 but only 110 in 2013 while team-controlled players produced only 478 wins in 1996 but 703 wins in 2013. The chart below shows the number of wins produced by each group annually from 1996 to 2013.

The cost of a win for each group of players has increased over the sample but the cost of a free agent and extended player win is increasing at a considerably larger rate than the cost of a team-controlled player win. The cost of a free agent win has increased from $1.04 million in 1996 to $7.03 million in 2013. The cost of an extended player win increased from $1.3 million in 1996 to $4.55 million in 2013. The cost of a team-controlled player win increased from $750,000 in 1996 to $1.88 million in 2013. The chart below shows how this changed over time.

Not only is the actual amount for a free agent win considerably more than the cost of a team-controlled player win but the annual rate of increase for a free agent win is more than twice that of a team-controlled player win. Over the entire sample, the value of a future team-controlled win increases by 5.6% (1.11/1.05) each year while for the past 10 years it has increased by 3.9% each year. Either way, this indicates that teams would save on player costs by keeping their prospects in the minors for extra time.

It appears that team-controlled players earning under a million dollars and those earning over a million dollars are seeing their cost per win increase at a similar rate. From 2005 to 2013, team-controlled players in each of these categories saw an annual increase of roughly 4%. I used 2005 to 2013 because players earning under $1 million were remarkably ineffective in 2004 while those earning over $1 million were remarkably effective. 2004 appears to be a clear outlier and therefore shouldn’t be used as a baseline.  This chart shows the change over time.



In addition, prospects do not appear to be receiving significantly higher signing bonuses.  According to the Associated Press, teams spent $150 million on the draft in 2004 and $208 million in 2013, meaning that signing bonuses have increased by only 4.3% per year over that period. This is similar to the increase in salaries for team-controlled players during that time period and therefore doesn’t indicate that teams are paying more in signing bonuses while paying less in salaries.

This indicates that both the minimum salary is too low and that team-controlled players don’t receive large enough salary increases in arbitration. In order to keep pace with free agent salaries they should be earning $3.4 million per win instead of $2.2 million per win and therefore their salaries should be roughly 50% higher. This raise in salary should come partially via an increase in the minimum salary and partially via an increase in arbitration.

Over the 17-year period, it is clear that team-controlled players are becoming considerably more valuable compared to free agents. The amount of money that it takes to purchase a win in free agency is growing at a considerably faster rate than the amount it takes to purchase a win with a team-controlled player -- team-controlled players produce nearly 70% of wins while free agents only produce 20%.

This is probably good for the game because it allows for greater parity. As it becomes more and more expensive to get value in the free agent market, then large market teams get less benefit for having a higher payroll. But it also means that prospects and other team-controlled players receive unfair compensation for their efforts.