First Base

I looked at which players had higher projected z-scores than Encarnacion, and whether they were available at the time I drafted Encarnacion in the fifth round.

all_final %>% 
  filter(position == 'first_base') %>% 
  top_n(., 10, tot_z) %>% 
  arrange(desc(tot_z)) %>% 
  select(Name, R, f_R, HR, f_HR, RBI, f_RBI, SO, f_SO, SB, f_SB, OPS, f_OPS, tot_z, f_tot_z) %>%
  knitr::kable()

This makes me feel a little better – the first basemen with higher projected total_z scores were drafted prior to my turn in the fifth. Most of them underperformed their projections, but still did well, except for Joey Votto, whose runs scored, home runs, and runs batted in are much lower than projected, leading to his abysmal final z-score this year. Other first basemen who were projected to perform worse than Encarnacion also underperformed their projection. All things considered, Edwin wasn’t a bad draft pick.

Outfield

I was really happy that I drafted Betts in the first round, given that he had high z-scores to begin with and then subsequently outperformed his projection. I looked at the next outfielder I drafted, who was Jones in the 12th round. Since all outfielders (except Brantley) with higher z-scores were drafted prior to my pick in the 12th round, I’ll exclude them from the table for simplicity’s sake.

all_final %>% 
  filter(position == 'outfield' & tot_z <= 2.550) %>% 
  top_n(., 10, tot_z) %>% 
  arrange(desc(tot_z)) %>% 
  select(Name, R, f_R, HR, f_HR, RBI, f_RBI, SO, f_SO, SB, f_SB, OPS, f_OPS, tot_z, f_tot_z) %>%
  knitr::kable()

Looking at both Brantley and Jones, Brantley was projected to do a bit better than Jones largely due to his low strikeout and high stolen bases projection. Jones was projected to handily beat Brantley in homeruns, and runs batted in. I probably selected Jones to boost my homerun numbers. But Jones had a pretty bad season, and Brantley outperformed his projections, so now I feel the pangs of regret. Both Mazara and Cain had already been drafted by the time I picked in the 12th round. Rosario had pretty similar projections to Jones, just with more strikeouts, and he also outperformed his projections.

The projections for these players aren’t that different from each other, so I’m kicking myself for not predicting the future, which is not constructive. I think the main message here is that I shouldn’t have waited this long to pick my other two outfielders. My third outfielder, Kepler, didn’t even break the top 30. Given what I know now about outfielders contributing to runs, homeruns, and RBIs, this is a huge shortcoming in my strategy.

Third Base

I drafted Moustakas late, in round 11. In the table, I filtered out players with higher z-scores who had been selected in earlier rounds of the draft, and took the top five since there are fewer third basemen.

all_final %>% 
  filter(position == 'third_base' & tot_z < 3.2) %>% 
  top_n(., 5, tot_z) %>% 
  arrange(desc(tot_z)) %>% 
  select(Name, R, f_R, HR, f_HR, RBI, f_RBI, SO, f_SO, SB, f_SB, OPS, f_OPS, tot_z, f_tot_z) %>%
  knitr::kable()

By the time I had a chance to draft Moustakas in the 11th round, Beltre and Gallo were still available. Given how much Beltre underperformed his projection, it looks like I dodged a bullet. Gallo would have been a better option for runs and home runs, but his strikeouts are really quite high (even after a bit of discipline this year, he still had twice as many strikeouts as Moustakas).

Shaw would have been another reasonable option – his z-score was quite low due to his high projected number of strikeouts, but his projected runs, homeruns, and RBIs are comparable to Moustakas’s, and he was also projected to steal more bases.

Ultimately, Moustakas did just fine relative to his projection, and I could have done well with either Gallo or Shaw.

I did also end up drafting Franco in a later round, which seems reasonable given his projected z-score. He underperformed his projection, however, and didn’t contribute much to my offense.

Second Base

All the players with higher projected total z-scores than Odor were already gone by the time I got picked up Odor in the 8th round.

all_final %>% 
  filter(position == 'second_base' & tot_z < 3.9) %>% 
  top_n(., 5, tot_z) %>% 
  arrange(desc(tot_z)) %>% 
  select(Name, R, f_R, HR, f_HR, RBI, f_RBI, SO, f_SO, SB, f_SB, OPS, f_OPS, tot_z, f_tot_z) %>%
  knitr::kable()

It’s worth noting here that none of the other second basemen would have been substantially better than Odor, based on the projections. Dee Gordon was projected to steal a lot more bases, but he was also projected to hit considerably fewer home runs, and bat in fewer runs. Additionally, none of these candidates outperformed their projections. Given that, I think Odor was the right choice here.

Catcher

Posey was the first catcher to be drafted, so every catcher was available to me at the time.

all_final %>% 
  filter(position == 'catcher') %>% 
  top_n(., 10, tot_z) %>% 
  arrange(desc(tot_z)) %>% 
  select(Name, R, f_R, HR, f_HR, RBI, f_RBI, SO, f_SO, SB, f_SB, OPS, f_OPS, tot_z, f_tot_z) %>%
  knitr::kable()

I’ve spent the past few posts kicking myself for drafting Posey, but I’m not sure the numbers merit drafting any other catcher in round two. The two catchers with higher projected z-scores (Sanchez and Gattis) would have hit more home runs and batted in more runs, but their strikeouts were also nearly twice that of Posey. The clincher is that both also ended up underperforming their projections.

Looking at the top 10 catchers, only a few substantially outpeformed their projections. Given that there isn’t much variation in this group (namely, most of them are pretty bad), this is probably a good reason not to draft catchers in the second round.

As a bonus, I did a little bit of exploration in my own league, and I found that most people don’t draft catchers in the first 10 rounds, because catchers don’t seem to make much of a difference. More reasons not to draft catchers so early.

Shortstop

I drafted Simmons in the fourth round, early enough that most shortstops were still available. I excluded the three that had already been picked (Correa, Turner, and Lindor).

all_final %>% 
  filter(position == 'short' & tot_z < 3) %>% 
  top_n(., 10, tot_z) %>% 
  arrange(desc(tot_z)) %>% 
  select(Name, R, f_R, HR, f_HR, RBI, f_RBI, SO, f_SO, SB, f_SB, OPS, f_OPS, tot_z, f_tot_z) %>%
  knitr::kable()

I discussed Bogaerts and Andrus in the previous post, so I’ll start with them. Bogaerts was projected to score more runs and hit more home runs than Simmons, and also strike out many more times. In the end, Bogaerts outperfomed his projections, batting in 33% more runs than his projections, and hitting more home runs, compensating for his high strikeouts. Andrus had similar projections, except he was also projected to steal more bases. He underperformed, so in retrospect, I’m glad I dodged that bullet.

Given Simmons’s low projected z-score, there are plenty of shortstops I could have done better with. Story’s projections were great, and he did even better by the end, cutting his strikeouts and stealing plenty more bases. Baez was also projected to do better than Simmons in homeruns and RBIs, and he outperformed his projections too, stealing more bases than expected.

Even though Simmons ended up outperforming his projection, it was as a result of fewer strikeouts, rather than increased runs. I would have done better with any of the others shortstops I mentioned, underscoring how big of a mistake it was to draft Simmons as early as I did. Down with WAR.