Frame: ESPN football yardlines

[
  {
    "team": "PHI",
    "period": 1,
    "clock": "6:15",
    "yardline": 99,
    "field_x": 49,
    "field_y": null
  },
  {
    "team": "PHI",
    "period": 2,
    "clock": "1:35",
    "yardline": 88,
    "field_x": 38,
    "field_y": null
  },
  {
    "team": "PHI",
    "period": 3,
    "clock": "2:40",
    "yardline": 54,
    "field_x": 4,
    "field_y": null
  },
  {
    "team": "KC",
    "period": 3,
    "clock": "0:34",
    "yardline": 24,
    "field_x": -26,
    "field_y": null
  },
  {
    "team": "KC",
    "period": 4,
    "clock": "2:54",
    "yardline": 7,
    "field_x": -43,
    "field_y": null
  },
  {
    "team": "KC",
    "period": 4,
    "clock": "1:48",
    "yardline": 50,
    "field_x": 0,
    "field_y": null
  }
]

import { SUPER_BOWL_LIX_TDS } from "@sportsdataverse/examples/data";
import { toSurfaceFrame } from "@sportsdataverse/sporty";

// ESPN plays: a 0-100 yardline from the home team's goal line (Super Bowl LIX's touchdowns, Philadelphia at home).
// The frame centres the field: midfield is x = 0. `x`/`y` pick the input columns and `out` names the outputs, so the
// frame fits any table. ESPN reports no lateral position, so these rows have no `y` and field_y is null, never NaN.
toSurfaceFrame(
  SUPER_BOWL_LIX_TDS.map(({ team, clock, period, yardline }) => ({ team, period, clock, yardline })),
  { from: "espn-football-0-100", x: "yardline", out: { x: "field_x", y: "field_y" } },
);

Tags: frames · toSurfaceFrame · espn-football-0-100 · football. All sporty examples.