In the article, I cover the players using CBS’s (about 40% or less initial roster rate) and Yahoo’s ADD/DROP rates. Both hosting sites have the option for daily and weekly waiver wire adds. CBS uses a weekly change while Yahoo looks at the last 24 hours. Yahoo is a great snapshot of right now while CBS ensures hot targets from early in the week aren’t missed. The players are ordered for redraft leagues by my rest-of-season preference grouped by starters, relievers, and hitters. Read the rest of this entry »
I have a short amount of time to get this done today (son’s final track meet) so I’ll give each team a quick look and move on. I’m not going to include any stats. I might look into lineups a little more at the meet on Twitter.
My issue was that even though Bibee’s walk rate was good in 2022 (combined minor league rate of 1.8 BB/9) there were signs that his walks could be an issue once this season started. While Bibee had some luck in 3-2 counts is one issue, I’m just going to focus on Ball% (Balls/Pitches). Read the rest of this entry »
While the NFBC Main Event garners most of the attention, there are a handful of leagues with even a larger entry fee ($2.5K to $15K). They get originally named “High Stakes Leagues” and this year there are nine of them. With so much money on the line, these fantasy managers are going to try to gain any advantage. Most of the time, these managers will be a week or two ahead of everyone else on their adds. Here are the players and some information on the ones added in four or more of these leagues. Read the rest of this entry »
Jeff Zimmerman: Here are the results from the two 15-team Tout Wars mixed leagues.
7:32
Jeff Zimmerman:
7:32
Jacks: 12 Team AL-Only, I’ve got $69 left (we allow $0 bids). Lost Urquidy and Luis Garcia this week leaving me with only Three Starters. Gausman, McClanahan and Bradish. How aggressive should I be on Bryce Miller? (Other SP on the wire are JP Sears, Louie Varland, JP France, Lorenzen, Battenfield, Pivetta, Kopech).
7:33
Jeff Zimmerman: The $0 bids help a ton to fill voids
7:34
Jeff Zimmerman: $30 to $40. Possibly more. I might set up my bids to go aggressive on Miller but get Varland and France/Sears as a backup
In the article, I cover the players using CBS’s (about 40% or less initial roster rate) and Yahoo’s ADD/DROP rates. Both hosting sites have the option for daily and weekly waiver wire adds. CBS uses a weekly change while Yahoo looks at the last 24 hours. Yahoo is a great snapshot of right now while CBS ensures hot targets from early in the week aren’t missed. The players are ordered for redraft leagues by my rest-of-season preference grouped by starters, relievers, and hitters. Read the rest of this entry »
WARNING: If you are reading this article, some or most the exact values are out of date. The pitch quality models seem to go through at least a yearly adjustment so I can’t verify if all the numbers will hold up. With that caveat, it’s useful to have an overall idea of what each one means.
Last week, I was looking into Joey Lucchesi and I created this convoluted mess of a table.
Joey Lucchesi’s Pitch Modeling Stats
Model
SI
CU/CH
FF/FC
Stuff
Overall
Bot
52
46
37
43
49
Stuff+
89
91
72
86
98
pERA (AAA)
5.54
-0.44
4.79
2.74
3.14
pERA (AAA comps)
4.72
2.89
4.23
–
–
To start off with, having three different metrics using three different scales is confusing. Not as obvious was that I didn’t know exact what the two “Stuff” metric were exactly measuring. I had some idea listening to their creators and others using them. I decided to take a step back and put some perspective on the two pitch quality models so others and myself could correctly reference them and know what other metrics they corelate to.
Note: When I mention stuff metrics, I’m just referring to the Stuff values for Stuff+ and Pitching Bot. I know it can be confusing, especially with one system having the name Stuff+.
Two start out with, this article won’t answer two questions. First, I’m not looking into the predictiveness of the stats. While I have done some work on it, I feel that should be its own article. Second, I’m just looking at the combined values, not the individual pitches. Again, a separate article for another day.
Here is a short description of each from the original articles.
PitchingBot
In short, PitchingBot takes inputs such as pitcher handedness, batter handedness, strike zone height, count, velocity, spin rate, movement, release point, extension, and location to determine the quality of a pitch, as well as its possible outcomes. Those outcomes are then aggregated and normalized on a 20-80 scouting scale, which is what is displayed on the leaderboards.
Stuff+
Stuff+ only looks at the physical characteristics of a pitch, including but not limited to: release point, velocity, vertical and horizontal movement, and spin rate.
…
Stuff+, Location+, and Pitching+ are all on the familiar “+” scale (like wRC+), with 100 being average.
While both supply a reason behind their values, it sucks that they each have their own scale. Personally, I have my pERA values and similar pitches on an ERA scale so there is readily recognizable reference.
The first item of business was to put put both of the metrics on an ERA scale. By lining up the values from 2021 and 2022 (min 40 IP) with the pitcher’s actual ERA, the following two formulas were created.
PitchingBot values to an equivalent ERA (r-squared of .992): 22.697*e^(-.035*Bot Metric)
Stuff+ values to an equivalent ERA (r-squared of .996): 49.19*e^(-.025*Stuff+ Metric)
With the two formulas, here is a quick reference table for stuff values and the ERA equivalent.
Conversion Table for ERA to “Stuff” Equivalents
ERA Equivalent
BotPlus
Stuff+
1.50
78
135
2.00
69
124
2.50
63
115
3.00
58
108
3.50
53
101
4.00
50
96
4.50
46
92
5.00
43
87
5.50
41
83
6.00
40
80
For an example, say a pitcher has a Stuff+ of exactly 100. We would expect the hitter to have an BotStuff around 52 and an ERA around 3.60.
The next step I did was bucket the three metrics for each PitchingBot (stuff, command, and overall) and Stuff+ (Stuff+, Location+, and Pitching+) and then compare them to other pitching metrics. To start with, here is a limited comparison (limited table size) with all the values in this Google Doc.
Pitching Bot
Comparison of PitchingBot’s Stuff to Other Metrics
Range
botOvr
botStf
botCmd
Pitching+
Stuff+
Location+
ERA
K/9
BB/9
WHIP
HR/9
>70
62
72
49
106
125
98
3.05
11.9
3.9
1.13
0.8
65-70
62
67
53
106
120
100
3.01
11.1
3.3
1.12
0.8
60-65
58
62
52
104
113
99
3.30
10.5
3.5
1.17
0.9
55-60
56
57
53
102
107
100
3.62
9.8
3.3
1.20
1.0
50-55
54
52
53
101
102
100
3.81
9.0
3.2
1.25
1.1
45-50
52
47
54
99
97
101
4.16
8.4
3.1
1.28
1.2
40-45
49
42
53
97
91
100
4.63
7.7
3.1
1.35
1.4
<40
46
36
54
96
86
101
4.60
6.7
2.9
1.36
1.3
Comparison of PitchingBot’s Command to Other Metrics
Range
botOvr
botStf
botCmd
Pitching+
Stuff+
Location+
ERA
K/9
BB/9
WHIP
HR/9
>70
62
44
70
101
88
108
4.06
9.4
1.4
1.11
0.9
65-70
64
50
66
105
104
107
3.75
8.8
2.0
1.16
1.1
60-65
59
50
62
104
101
104
3.59
9.0
2.3
1.16
1.2
55-60
56
50
57
102
101
102
3.85
8.7
2.7
1.21
1.1
50-55
53
52
52
101
103
100
3.83
9.1
3.3
1.26
1.1
45-50
49
51
47
98
99
97
4.37
8.7
3.9
1.36
1.1
40-45
46
53
42
97
102
95
4.28
9.3
4.5
1.35
1.1
<40
43
56
35
96
104
92
4.11
9.6
4.6
1.37
1.0
Comparison of PitchingBot’s Overall to Other Metrics
Range
botOvr
botStf
botCmd
Pitching+
Stuff+
Location+
ERA
K/9
BB/9
WHIP
HR/9
>70
72
69
61
112
128
105
2.39
11.9
2.2
0.95
0.8
65-70
67
62
61
109
118
104
3.07
10.7
2.4
1.05
1.0
60-65
61
58
58
105
110
103
3.41
10.2
2.7
1.13
1.0
55-60
57
54
56
102
104
101
3.59
9.3
3.0
1.21
1.0
50-55
52
49
53
100
99
100
4.05
8.5
3.2
1.29
1.2
45-50
47
45
50
97
93
99
4.40
8.1
3.6
1.34
1.2
40-45
42
44
45
95
92
97
4.87
7.8
4.0
1.43
1.3
<40
37
47
38
93
94
93
4.84
8.6
4.8
1.43
1.2
Stuff+
Comparison of Stuff+’s Stuff+ to Other Metrics
Range
Pitching+
Stuff+
Location+
botOvr
botStf
botCmd
ERA
K/9
BB/9
WHIP
HR/9
>130
111
137
102
65
68
54
2.38
12.5
2.6
0.92
0.9
125-130
107
127
100
61
65
52
2.97
12.0
3.4
1.12
1.0
120-125
106
121
99
61
65
52
3.02
10.2
3.3
1.12
0.7
115-120
106
117
101
60
62
54
3.24
10.8
3.3
1.14
0.9
110-115
104
112
100
58
57
54
3.25
10.0
3.2
1.16
1.0
105-110
102
107
100
54
54
52
3.63
9.5
3.2
1.20
1.0
100-105
100
102
100
52
51
52
3.87
9.1
3.4
1.25
1.1
95-100
99
97
100
52
49
53
4.17
8.4
3.2
1.30
1.2
90-95
98
92
100
49
45
53
4.49
8.0
3.2
1.33
1.3
85-90
96
87
100
49
44
53
4.78
7.4
3.3
1.41
1.3
80-85
95
82
100
48
41
53
4.64
6.9
2.9
1.38
1.4
75-80
94
78
101
46
38
54
4.64
6.8
3.0
1.39
1.3
<75
92
70
101
46
37
55
5.97
6.0
2.9
1.54
1.6
Comparison of Stuff+’s Command+ to Other Metrics
Range
Pitching+
Stuff+
Location+
botOvr
botStf
botCmd
ERA
K/9
BB/9
WHIP
HR/9
105-110
105
104
106
61
50
63
3.49
8.9
2.0
1.13
1.1
100-105
102
101
102
55
50
56
3.83
8.9
2.8
1.22
1.1
95-100
98
100
97
50
52
48
4.14
8.8
3.8
1.32
1.1
90-95
96
104
93
45
57
41
4.44
9.9
5.1
1.44
1.0
85-90
95
106
87
41
63
33
4.32
11.3
5.3
1.26
0.8
Comparison of Stuff+’s Pitching+ to Other Metrics
Range
Pitching+
Stuff+
Location+
botOvr
botStf
botCmd
ERA
K/9
BB/9
WHIP
HR/9
>115
116
140
105
72
69
60
2.35
12.3
1.7
0.83
0.9
110-115
111
125
104
67
64
59
2.84
11.3
2.4
1.00
0.9
105-110
107
114
102
61
58
57
3.16
10.2
2.7
1.10
1.0
100-105
102
104
101
55
53
54
3.65
9.2
3.1
1.22
1.0
95-100
97
94
99
49
47
51
4.30
8.2
3.5
1.33
1.2
<95
93
87
96
43
44
46
5.38
7.5
4.1
1.51
1.4
Looking over the information, both of the “Stuff” values seems to generally catch what each is trying to describe. The stuff values corelate to strikeouts and the command/location grades point walk rate.
After reading through the definitions of how the batted ball data is collected, I expected the Bot values to have a larger variance in the StatCast values (linked spreadsheet). That concept wasn’t the case and there ended up being almost not correlation to any of the measures to actually limiting hard contact. With hard contact not being predictive, I was surprised when I got to WHIP.
How much WHIP changed in the two “Stuff” models was almost too good to be true. In both cases, the walk rate increased as a pitcher’s stuff got better, but the hit suppression was so large that the WHIP declined.
The ability to detect hit suppression is on another scale than has ever been measured. It’s almost too good to be true.
Overall, I see two major issues with the stuff metrics:
The formulas behind the values is a black box so there is no way to back check the results. Also, the calculations are constantly changing so it’s tough to know which formula is being used. It’ll be impossible to incorporate the information if it keeps changing
The pitch model metrics are trained off of just the 2021 and 2022 data. Of course the data is going to almost lineup perfectly for now. It’ll be interesting to see how they hold up this season and three to four seasons down the road.
The next step for me will be to dive into the small sample of 2023 data. Does the near perfect accountability of all batted ball outcomes continue based on just pitch metrics or were the metrics correlated too close to the actual results I’m examining.
I did get a few questions answered but working through these Pitch Quality Models but I generated a ton more. As I get time, I’ll keep diving into the subject to see what is usable going forward.
That’s the big flaw with the sweeper. It had the second-biggest platoon split among all the pitch types — the difference in production between same- and opposite-handed hitters is the second-largest in baseball over the last two years.
…
So what do you do with a pitcher that has a great weapon against same-handed hitters and needs something for opposite-handed hitters? Scan down to the bottom of that list. The oldest answer in baseball: Get a changeup. The changeup is still the best way for a righty to get lefties out.
While the NFBC Main Event garners most of the attention, there are a handful of leagues with even a larger entry fee ($2.5K to $15K). They get originally named “High Stakes Leagues” and this year there are nine of them. With so much money on the line, these fantasy managers are going to try to gain any advantage. Most of the time, these managers will be a week or two ahead of everyone else on their adds. Here are the players and some information on the ones added in four or more of these leagues. Read the rest of this entry »
