# Jam Expectancy Study: Pack Situations

If you read our introduction article posted last week, you should now have a pretty good understanding of the concept of expectancy and how it might apply to roller derby. We’re trying to determine what results, on average, a team can expect in different situations.

At the beginning of a jam, each team lines up with 1-4 blockers between the pivot and jammer lines. The number of blockers each team has as of the double-whistle is being represented in this study by the stat: iPS (initial pack strength). The number of pack skaters varies depending on penalties, and we’re using the iPS stat and jam outcomes to calculate average results. In the 20 bouts included in the study, we had a total of 847 individual jams. Taking into account both teams in every jam, that’s 1694 total samples.

Let’s start out by taking a look at how often you’ll see of all the possible pack situations. The following chart represents the frequency of each pack situation over the 20-bout study, in percentage of total jams:

Notice that a 3-pack is more common than a 4-pack over the course of a bout. In fact, teams have 4 blockers on the track only about a third of the time. The most common pack situation is 3-vs-3.

Now that we have an idea of how common each pack situation is, let’s look at the expected results. PLEASE NOTE: I am excluding jams where any team started on a power jam from the following charts. The purpose here is to get an idea of what odds are facing teams in different pack situations. When one team is on a power jam, odds are altered and the averages for non-power jams no longer matter. By the same token, power jam averages are irrelevant when both jammers are on the track. I’m also excluding 1-pack situations from the following charts due to the extremely small sample sizes.

The following table represents average jam differentials in non-power jam situations over the 20-bout study:

Check out the differences here between even packs and uneven packs. It appears that having any advantage is more important than how big the advantage is. 4-vs-3 and 3-vs-2 situations yield very similar results. With a 1-skater advantage, you are going to average just under 2.5 points over your opponent. Having a 2-skater advantage actually isn’t that much better. A 4-vs-2 advantage is less than a point better than either scenario with 1-skater advantage.

Let’s take a deeper look into how teams achieve those differential numbers. This next chart represents the average lead jam percentages in non-power jam situations over the 20-bout study:

It still appears that having any advantage in the pack is the most important thing, but it’s not as simple here. The differential for a 3-vs-2 advantage is similar to 4-vs-3, but the lead jam percentage for 3-vs-2 is not as strong. I suspect that is a result of the thinner packs. Logically, you would assume that the average time a jammer is stuck in the pack decreases as the pack is thinned by penalties. Because the average time it takes a jammer to escape the pack is less, one good jammer force out can have a greater impact on who gets lead. Chances are greater that your jammer will escape the pack in the time it takes the opposing jammer to re-enter the pack legally. The differentials remain similar because ghost points are still easier to earn than lap points, but lead jammer percentage decreases somewhat.

Let’s take a look at this from another angle. The following two charts represent the frequency of jam wins and jam ties to each scenario. You can also look at the opponent’s win percentage to see your loss percentage:

By far the most common score in a jam tie is 0-0. Jam ties are usually the result of the jammer with lead being forced to simply call off the jam to avoid the possibility of being outscored. That can result from the opposing pack holding the front, but most often it happens when both jammers get out of the pack at nearly the same time. The tie percentages start off near the overall average in 4-vs-4 packs. When 1 or 2 skaters are removed, the tie percentage actually decreases slightly. As the pack becomes even thinner, however, the tie percentage skyrockets. I would suspect this is because neither jammer can be held in the pack for very long.

Comparing 4-vs-3 and 3-vs-2 situations here also supports the theory that thinner packs mean quicker passes. There is a difference in jam win percentage between the two 1-skater advantage situations, and it again favors the 4-vs-3 advantage where packs are thicker. What’s interesting here is that the loss percentages for these two scenarios are very similar. The reduction of jam wins in the 3-vs-2 situation is more a result of an increase in jam ties. This also leads me to believe that the thinner packs are resulting in quicker passes, and thus, more simultaneous initial passes.

So what big thing do I take away from all of this? Penalties matter a lot. Yes, it’s a cliché. I listened to announcers on the Big 5 feed say it over and over again. Still, I didn’t realize just how much penalties really do matter. I’ve heard people dismiss the loss of only 1 blocker to the penalty box as not being a big deal, and I had always agreed. I assumed that if a team has 3 blockers, they can still operate at near peak effectiveness. It’s not a big problem unless you drop down to only 2 blockers, right? Scroll back up and look at the differentials for each pack strength. Overall, the loss from 4 to 3 is bigger than it is from 3 to 2.

No stat is perfect. iPS doesn’t track blocker penalties that are served entirely within a single jam. Many carry-over penalties extend into a third jam as well. Still, I think you can learn a lot by using objective stats. I have provided some personal analysis here, but there is definitely more theorizing that can be done here. What is the effect of pack speed? How does strategy change in different pack situations? Feel free to draw your own conclusions and share them in the comments.

In the next article, we will be examining power jams. How much do they really effect a bout? What is the average power jam worth in terms of points? We’ll try to come up with some objective answers and weigh them against the “conventional” thinking.

See you next time. Same stat time. Same stat channel.