If you have been following the work on the site all year you have certainly taken a look at Crosby Spencer’s Schedule Factor articles. Every week Crosby looks at the week ahead and ranks hitter/pitcher matchups based on RotoFanatic’s own Park Factors. His article typically is released every Saturday morning. With that general idea in mind, we have decided to work with the Data monster statistics to build out similar Schedule Factors.

In order to do this, I needed to rework the Data Monster calculations to look at the team level instead. This allowed me to get the individual impact of each team on both the offensive and pitching sides of things. The most interesting aspect of this to me is that thus far in the season we do not have enough data to determine In_wOBA on a team level so, for now, we will only be looking at In_Whiff, IZ, and OOZ at the team levels.

 

Hitter Data Monster Team Factors
Through Week 7
Team In_Whiff Rank IZ Rank OOZ Rank
ARI -0.3 10 -1.8 26 -3.7 2
ATL 0.6 24 3.2 1 0.6 19
BAL 0.5 23 2.5 4 3.1 28
BOS -0.4 9 1.4 7 5.4 30
CHC 0.3 19 -1.4 24 -0.4 13
CIN 1.1 27 1.3 8 0.4 16
CLE -0.2 13 0.7 11 0.9 22
COL 0.4 20 1.9 6 0.7 21
CWS 0.0 17 -1.0 21 2.7 27
DET 1.6 30 -0.1 13 2.3 25
HOU -2.3 1 -0.6 19 -1.1 9
KC -0.1 15 2.9 3 0.9 23
LAA -0.7 5 -0.8 20 2.5 26
LAD -1.0 3 -0.2 15 -3.8 1
MIA 0.2 18 -2.6 29 3.8 29
MIL 0.8 26 -2.6 28 -1.5 8
MIN -0.2 12 2.4 5 0.1 15
NYM 0.8 25 3.1 2 0.6 20
NYY 0.0 16 -0.4 18 -3.1 3
OAK -0.3 11 -1.5 25 -2.4 5
PHI 1.4 29 1.0 10 0.6 18
PIT -0.1 14 0.3 12 -1.1 10
SD -1.3 2 -3.4 30 -1.0 11
SEA 0.4 22 -0.3 17 -2.2 6
SF -0.7 6 -2.5 27 -2.7 4
STL -0.9 4 -0.2 16 1.2 24
TB 1.1 28 -0.1 14 -2.1 7
TEX 0.4 21 -1.1 22 0.4 17
TOR -0.6 8 1.3 9 -0.1 14
WSH -0.6 7 -1.3 23 -1.0 12

Above are the team-level factors for offenses including their ranks through Yesterday’s games. So how exactly do we read these? Let’s use Houston as an example:

  • There have a team level In_Whiff of -2.3 (best in baseball): This means that they swing and miss 2.3 percentage points less often than one would expect based on the pitches they saw
  • Their IZ is -0.6%, meaning they swing in the zone slightly less often than expected
  • Lastly, their OOZ is -1.1%, meaning they chase about 1 percentage point less often than we would expect

Thus the Astros do not chase often and do not swing and miss making them an extremely difficult offense to face. We can then do the same with pitchers, see the factors below. This can be used the exact same way as the factors for hitters above.

Pitcher Data Monster Team Factors
Through Week 7
Team In_Whiff Rank IZ Rank OOZ Rank
ARI -0.9 25 0.3 21 -1.1 24
ATL 0.1 13 0.3 22 0.6 11
BAL 0.5 8 0.0 14 1.2 4
BOS -0.4 20 -0.1 13 -1.3 25
CHC -1.1 28 -1.1 2 -1.5 29
CIN 0.1 11 -0.9 3 -0.2 16
CLE 0.1 12 0.5 25 -1.4 26
COL -1.1 29 0.5 24 -3.1 30
CWS 1.5 1 0.5 23 1.5 3
DET -1.3 30 0.2 18 -0.9 22
HOU -0.3 18 -0.3 11 -1.0 23
KC -0.4 19 -0.4 8 -1.4 28
LAA 0.9 6 -1.2 1 0.4 13
LAD 1.4 2 0.2 20 2.8 2
MIA -0.2 17 0.2 19 0.8 7
MIL 0.5 9 -0.7 5 0.5 12
MIN -1.0 27 0.6 26 -0.4 18
NYM 1.3 4 0.6 28 1.1 5
NYY 1.1 5 -0.3 9 3.1 1
OAK 0.3 10 1.1 29 -0.5 20
PHI -0.4 21 0.1 15 1.0 6
PIT -0.6 22 -0.6 6 0.7 9
SD 1.3 3 -0.8 4 -0.4 19
SEA -1.0 26 -0.1 12 0.6 10
SF -0.7 24 -0.3 10 -0.5 21
STL -0.6 23 0.6 27 -1.4 27
TB 0.7 7 0.2 17 0.2 14
TEX 0.0 16 1.2 30 0.8 8
TOR 0.0 14 -0.4 7 0.0 15
WSH 0.0 15 0.1 16 -0.2 17

Schedule Factors

Now that we have the team-level factors we can then match them up with schedules in order to develop schedule factors. These can be extremely useful for start sit decisions as well as making pickups for the week.

Pitcher Data Monster Schedule Factors
Based On Week 7 Schedule
Team In_Whiff Rank IZ Rank OOZ Rank
ARI -0.14 20 -2.08 2 1.76 4
ATL 0.11 12 -0.63 11 -0.80 23
BAL 0.19 10 1.08 23 -0.43 19
BOS -0.35 25 -0.62 12 0.50 11
CHC 0.87 3 0.22 17 1.70 5
CIN 0.15 11 1.23 25 -0.05 17
CLE 0.37 8 -0.65 10 -1.62 28
COL 0.05 15 -0.71 9 -0.20 18
CWS -0.14 21 2.65 30 0.49 12
DET 0.11 13 0.75 20 0.27 14
HOU -0.09 17 -0.95 7 1.32 7
KC 0.79 5 -0.56 13 2.49 2
LAA -1.33 29 0.38 18 2.15 3
LAD 0.30 9 -1.69 3 1.40 6
MIA -0.58 27 -1.14 6 -3.72 30
MIL -0.11 18 1.46 27 0.87 9
MIN -0.13 19 -1.27 4 0.16 15
NYM 0.90 2 0.94 22 -0.03 16
NYY 0.83 4 1.20 24 0.49 13
OAK -0.31 24 1.88 28 2.74 1
PHI -0.61 28 -0.02 16 -0.53 20
PIT 0.08 14 -0.89 8 -1.38 26
SD -0.25 22 0.84 21 0.94 8
SEA -0.44 26 0.42 19 -0.68 22
SF 0.05 16 -0.16 15 -0.57 21
STL -0.25 23 -2.99 1 -1.28 25
TB 0.38 7 1.36 26 -1.23 24
TEX -1.73 30 -1.26 5 -1.64 29
TOR 1.01 1 2.10 29 0.57 10
WSH 0.55 6 -0.39 14 -1.54 27

Once again let’s use an example, this time looking at the pitching side of things. Let’s say I am looking at pitchers to pick up for the week ahead and want to consider Mike Foltynewicz. I would consult the chart above and I would see the following three notes:

  • Their matchup-based In_Whiff is -1.73, the lowest for the week ahead
  • Their IZ is -1.26, 5th best for the week ahead
  • Lastly, their OOZ is -1.64, second-worst

So what does this mean for Folty? While he has been decent enough so far, he is being hit extremely hard, and facing a lineup that makes much more contact than the average lineup is a bad omen for the Rangers’ right-hander. On the flip side, Kansas City gets lineups that swing and miss more often than usual, take within the zone more often than expected, and chase more often. This a great set-up for Royals’ pitchers and if any are available they are potentially poised for a big week.

Looking at the hitting side of things below, we can see that the Reds may be set up for a huge week. They are facing pitchers who struggle to generate whiffs and for an offense with the kind of pop Cincinnati possesses, more contact should lead to better results. Tampa Bay could be in line for a bad week as not only do they swing and miss for than expected, they are facing pitching staffs that generate a ton of whiffs.

Hitter Data Monster Schedule Factors
Based On Week 7 Schedule
Team In_Whiff Rank IZ Rank OOZ Rank
ARI -0.11 16 0.14 13 0.39 20
ATL 0.24 20 -0.54 26 0.26 18
BAL 0.92 29 0.04 18 1.70 29
BOS 0.58 23 -0.01 21 0.12 17
CHC -0.72 4 0.29 6 -1.09 5
CIN -0.86 2 0.03 19 -1.50 2
CLE -0.99 1 -0.48 25 -0.08 13
COL 0.64 25 -0.83 30 -0.27 11
CWS -0.71 7 0.11 14 -0.91 6
DET -0.72 5 -0.76 29 -1.45 3
HOU 0.35 21 0.18 10 0.60 23
KC 0.10 18 0.31 5 0.32 19
LAA -0.37 12 -0.17 23 -1.15 4
LAD -0.49 9 0.05 17 0.75 27
MIA 0.13 19 0.26 7 0.56 22
MIL -0.24 14 0.47 3 -0.41 9
MIN 0.90 27 0.80 1 0.52 21
NYM 0.64 26 0.08 15 0.60 24
NYY 0.62 24 0.07 16 0.69 25
OAK -0.72 6 0.26 8 -0.83 8
PHI 0.00 17 -0.15 22 -0.11 12
PIT -0.34 13 -0.55 27 -0.36 10
SD -0.84 3 0.55 2 -2.27 1
SEA 0.56 22 0.42 4 0.00 15
SF -0.38 11 0.01 20 0.71 26
STL 0.90 28 -0.73 28 0.06 16
TB 1.20 30 0.16 12 2.10 30
TEX -0.46 10 -0.28 24 -0.84 7
TOR -0.16 15 0.19 9 0.79 28
WSH -0.65 8 0.17 11 -0.06 14

Like any schedule factors, these are not meant to be used as gospel and are instead meant to be tools when making any final lineup-based decisions or waiver adds. This is going to be a fluid process all season and I’ll constantly be making changes to improve however I can.