Pargat Singh is a batter with 4 seasons of T20 data, overwhelmingly from T20I (98%) with barely 2% from SMAT. He has never appeared in the IPL. The extreme T20I tilt in his format breakdown means his model projection draws heavily on international data, with SMAT serving as a small supplement.

The SMAT numbers are limited: 165 runs off 156 balls across 6 innings. The strike rate of 106 is a significant concern for IPL aspirations. Even the most conservative IPL batting role requires tempo above 130, and 106 is closer to Test match scoring rate than T20 franchise cricket. The average of 28 is respectable but does not compensate for the lack of scoring speed.

One secondary data point: he has bowled 18 balls in SMAT, taking 2 wickets at an economy of 6. That sample is far too small to assign bowling value, but it hints at a secondary skill that could add marginal utility.

The model projects 0.24 WAR/M for 2026 at medium confidence, with a rising trajectory. Most of his WAR signal comes from T20I data, not SMAT. The current WAR/M of 0.35 is significantly above the projection, reflecting heavy regression toward the mean given the limited domestic sample. That gap between current and projected values underscores how much the model discounts small SMAT samples.

The rising trajectory suggests recent performance has improved, but the baseline is low. At 106 SMAT SR, Singh would rank among the slowest scorers in any IPL squad. A breakout SMAT campaign pushing his SR above 135 would change the conversation, but 6 innings of evidence at the current rate makes that outcome speculative.

Singh needs to demonstrate he can score at T20 tempo against quality bowling. Until the SMAT SR moves, the T20I data alone is not enough to project IPL viability with confidence.

All numbers from SMAT and domestic T20 career data.