The Monday Morning Agent 用数据字典定义橄榄球 tush push 战术

“Tush push” never appears in our roughly 265,000 play descriptions from 2021 through the current season. Neither does “brotherly shove,” and “sneak” turns up only twice. So when we asked our AI analyst to tackle “Ban the Tush Push” in the October 26 episode of The Monday Morning Agent, we first had to define the play using the available data. We saved that definition in a shared data dictionary, the agent’s first durable memory, so future runs must use the same definition and explain its limitations.

Check whether the data supports your definition

A quarterback takes the snap, and teammates push him forward. Our data identifies the ball carrier but doesn’t record whether teammates pushed him. We can check whether a snap came from shotgun, but we can’t reconstruct the team’s specific formation.

We explored five possible definitions, from a quarterback sneak with teammates pushing to any rush on third or fourth and short. Each definition changes which plays the analysis counts. The broadest one would count running backs too, which takes us away from the play people are arguing about.

So before you let an agent query a term everyone seems to understand, write its definition in plain language. Then go clause by clause and ask whether the data can support it. A familiar name doesn’t guarantee a usable label.

Write down what your definition can’t measure

We settled on a quarterback run with one yard or less to go, from under center. The agent counts qualifying quarterback sneaks whether anyone pushes the runner or not. We’re measuring a substitute for the tush push, and we can’t isolate the shove’s contribution.

Even “quarterback” needs a definition. The fields available to our tool don’t include player position, so the tool identifies the quarterback as the player who threw the most passes for that team in that game. That’s another assumption to check when you interpret the results.

The dictionary entry has to explain the play in plain language and state what the data can’t tell us. Otherwise, readers could assume a “tush push” result counts plays with a push. Keep the limitation beside the definition so the agent can repeat it with the result.

Give the next run the same definition

The dictionary holds one JSON file per definition, with a fixed structure. The agent can add an entry, but it can’t change an existing one. A different definition needs a new name. That keeps the meaning attached to an earlier result from quietly changing later.

Before each run, the script that launches the agent, called the runner, saves a copy of the dictionary and adds its contents to the agent’s instructions with the date. The agent searches for an existing definition first. If it finds one, it uses it. If it doesn’t, it can write a new entry using the fields the tool supports.

In the demonstration, the tool refused the request for “tush push” because no entry existed. The agent then created a definition and queried it.

Check that the agent uses its memory

Writing “use the dictionary” in a policy doesn’t make the agent obey. We’re building this around instruction, check, and enforcement. The policy tells the agent to use and cite an entry. The tool accepts definitions from the dictionary rather than a definition improvised in a query. If the answer lacks the required ruling, the runner retries once, then stops if it’s still missing.

We expanded the agent’s permissions to let it add a dictionary entry alongside querying the plays. Test whether your agent uses its shared definitions and whether someone reading the answer can find them.

Read the conversion rate before deciding on a ban

Across five complete seasons, the defined play converted 78.3% of the time, compared with 70% for other short-yardage rushes. That’s an 8.3 percentage point advantage.

While the agent found an advantage, it didn’t treat the conversion gap as sufficient evidence for a ban. Those numbers describe conversion rates for our defined group of plays. They don’t establish whether the NFL should ban a play, and they can’t tell us how much pushing helped.

A viewer raised that distinction during the discussion. Whether a successful play belongs in the game requires human judgment. Conversion rates can inform that conversation, but they can’t resolve questions about safety or what people want football to look like.

Next up: “Kickers Are Too Good Now”

On November 9, we’ll tackle “Kickers Are Too Good Now” and give the agent written visualization standards. An agent can generate a chart in seconds. We’ll work on rules for presenting trends and making comparisons so readers can see what the data actually supports.

O’Reilly members can sign up on the O’Reilly learning platform to join us live on November 9, 8:00am–8:45am PT / 11:00am–11:45am ET. Everyone can follow along on Radar every other Monday, with a brief pause for the holidays. Watch episode two above, and we’ll build on it in the next session.