I believe data can be divided into few segements for prediction, trends and historic reference.
I have created two kind of data for my work
A) – SBDI – skilled based data – This features all type of skills that consitutues a performance, from defense intention to fielding awarness, to bowling variation. This also can be called as qualitiative data!.
B) – SBD2 – Statitic based data – These data cannot be controlled hence they have secondary purpose to the outcome of a a performance. For instance the toss factor, venue factor, first batting/second batting, lead factor, bowling ends, weather factor, reduced match, type of bowlers, Right-hand/left-hand batsman, umpires, pitch condition, size of ground. Any information that cannot be easily altered or changed is SBD2.
I have hardly published anything that is related to SBD1 – however here is a sample of one such match that is SBD1
Below graphic features these indicators
- Attacking intention : this includes all type of intention to attack including errors
- Batting error : this stat features batting errors including dismissals, edges, uppish shots, top-edges, under-edges, miss-timed, miscue shots, beaten, missed the ball,
- Defensive outcome – this stats includes – all type defensive shots(open/close/cramp), leaving of the ball, push shots, steer the ball, turn of bat, ducking the ball, dugout
- Gaps found – any ball that passes two fielders in 5 meter distance in 30-yard circle, and 10 meter outside circle.
- Ariel shots – pull, hook, upper cut, loft, slash, ramp, scoop, in-out loft, flick-loft, shuffle-swing, swing, slap, topedge,
- Pyro technique – ramp, scoop, reverse-sweep, shuffle-paddle, shuffle-swing, upper-cut, flick-loft, in-out,
- % Bowling variation: Yorkers, bouncers, slow/wide bouncer, googly, armball, all type of slower ball, Yorkers, beamer, cutters(in/out), big-turners, top-spin, flipper, flight, late-movement
Batting stroke outcome
Bowling outcome
