Bet the data, not the vibes.

In this walkthrough, Pips, a professional sports bettor, runs through his full PropsMadness research process for two MLB markets: home runs and strikeouts. He opens by checking his tracked results on an outside tracker, where his home run bets have run at a 7% ROI over the past two months.

For home runs, he starts on the slate-wide home run environment reading for each game and skips anything strongly negative, favoring matchups closer to neutral or positive. From there he checks the opposing starter's home run and barrel rate allowed, looking for pitchers trending worse lately even if their season numbers look average.

His featured example is a hitter facing a struggling starter with weak home run prevention. Breaking the pitcher's arsenal down by pitch type shows the batter grades in the 90th-plus percentile against exactly the two pitches that starter throws most, while the pitcher barely throws the one pitch the batter handles worst.

He layers on more context: the hitter's plate appearances per game have climbed as he's moved up the batting order, his rolling home-run rate is far outpacing his season average, and in games with a similar park/weather environment to this one he's already homered in 10 of his last 16. He closes the pick by comparing odds across books to find the best price before betting.

Switching to strikeouts, his first example is a starter pitching in a favorable park who faces a lineup that's strong against right-handed pitching overall but ranks near the bottom against left-handers specifically. Filtering the pitcher's own game log to only the match ranked opponents shows his strikeout rate collapsing well under the posted line in that split.

A second strikeout example uses a full three-season sample for a different starter: filtered down to games against bottom-tier strikeout matchups, he clears the line in the large majority of starts, while the same filter against tougher lineups shows the exact opposite. The takeaway he leaves viewers with: the matchup-specific filtering is what turns a season average into an actual edge.