THE PLAY: USC -180

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#4 All-Sports Profit 2025 (+49u)

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_______

USC vs Cal Poly (2/27)

Probable starters

• USC: LHP Mason Edwards (2-0, 0.00 ERA)

• Cal Poly: RHP Griffin Naess (1-1, 1.50 ERA)

Form snapshot (what’s driving the numbers)

USC (7-0)

• Runs: 49 scored / 13 allowed through 7 games

• Team offense: .246 AVG, 6 HR, .370 OBP (early but productive)

• Team pitching: 1.68 ERA, opponents hitting .157

• Starter Edwards: 0.00 ERA in 11.0 IP, 20 K / 4 BB, opp AVG .029

• Bullpen/leverage: Adam Troy 0.00 ERA with 4 saves already (big “close games don’t stay close” signal)

Cal Poly (4-3)

• Runs: 41 scored / 28 allowed (more volatile run prevention so far)

• Team offense: .284 AVG (they hit), but fewer total runs than USC

• Team pitching: 3.14 ERA, opponents hitting .217

• Starter Naess: 1.50 ERA in 12.0 IP, 18 K / 3 BB, opp AVG .098

• Bullpen: Nick Bonn has a save (SV=1) but overall staff results haven’t matched USC’s run suppression

Batter form (quick “who’s hot”)

• USC: Kevin Takeuchi .450, Abbrie Covarrubias .391

• Cal Poly: Alejandro Garza .433, Casey Murray Jr. .360

Win probability + “my line” (moneyline)

My projected win probabilities

• USC: 82%

• Cal Poly: 18%

Equivalent American odds (from my projection)

• USC: -456

• Cal Poly: +456

(USC gets a big edge from the combined profile: run differential, staff dominance vs opponent AVG, and a bullpen that’s already converting saves at a high clip.)

Compare to the book & calculate value

Book implied probabilities (no-vig not removed)

• USC -180 ⇒ 64.29%

• Cal Poly +140 ⇒ 41.67%

“Value” (projected probability − implied probability)

• USC: 82.00% − 64.29% = +17.71%

• Cal Poly: 18.00% − 41.67% = -23.67%

EV per $100 stake (for intuition)

• Bet USC -180: profit $55.56 if win → EV ≈ +$27.56 / $100 stake

• Bet Cal Poly +140: profit $140 if win → EV ≈ -$56.80 / $100 stake

Lean: USC moneyline (by my numbers) is the only side with value versus -180/+140, mainly because USC’s pitching + bullpen has been suffocating and Edwards’ miss-bats profile is elite so far.

Here’s the case for USC moneyline (-180)

1) USC’s run-prevention profile is obscene so far

USC through 7 games:

• Team ERA: 1.68

• Opponents batting: .157

• WHIP: 0.97

• K/9: 13.7 (USC’s own preview notes this as elite nationally)

That combo matters for ML because it shrinks the upset paths:

• fewer “oops” innings,

• fewer balls in play (Ks erase BABIP luck),

• and it forces the dog to string together hits (harder than running into one 3-run bomb).

2) If Mason Edwards starts, USC has a “starter mismatch” edge

Edwards’ early line:

• 11.0 IP, 1 H allowed

• 20 K / 4 BB

• 0 ER

Cal Poly can hit (.284 team AVG), but this is a very specific type of opponent: a high-K, low-contact staff.
When you’re betting ML, aces are leverage: they reduce variance by preventing the “random early crooked number.”

3) Cal Poly’s starter (Naess) is legit… and that actually strengthens the ML angle (weirdly)

Naess has been excellent:

• 1.50 ERA, 12.0 IP, 18 K, 3 BB, opp AVG .098

So why is that good for USC ML?

• Because it can suppress scoring for both teams, turning the game into a bullpen + defense + late-execution contest.

• And USC looks built for exactly that.

4) Late innings: USC’s “close-game closer” advantage

USC has already stacked high-leverage outs:

• Adam Troy: 4 saves, 0.00 ERA, 11 K in 5.0 IP

Cal Poly’s pen is fine, but it’s more mixed:

• Bonn has a save, but several relievers sit in the 4+ ERA range early, and the staff overall is 3.14 ERA with opponents hitting .217.

If this is tied or one-run in the 7th–9th, I’d rather be holding the ticket attached to the team that’s already showing it can slam doors.

5) USC creates more “instant offense” (important when facing a good starter)

Through 7 games:

• USC: 6 HR, 49 runs (run differential +36)

• Cal Poly: 2 HR, 41 runs (diff +13)

Against a guy like Naess, rallies are hard. HRs (and extra-base damage) become a cheat code. USC’s early power edge matters.

6) Defensive/cleanup edge: USC has been cleaner

USC: 3 errors total so far.
Cal Poly: 10 errors total so far.

Errors are underpriced chaos. In low-scoring games, one extra out is basically a coupon for a run.

7) Price check: -180 implies a win rate USC has already exceeded in underlying quality

• USC -180 implied win% ≈ 64.3%

• Your bet wins if USC is materially better than ~64/36 in this matchup.

Given:

• the gap in opponent AVG allowed (.157 vs .217),

• the elite K profile,

• and the late-inning closer edge,
  …it’s not crazy to argue USC’s true win% is higher than the book’s implication.

THE PLAY: San Francisco Giants -185

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#1 All-Sports Profit 2024 (+180u)
#1 All-Sports Profit 2022 (+125u)
#1 All-Sports Profit 2021 (+225u)
#4 All-Sports Profit 2025 (+49u)

Tokyo Brandon has finished #1 overall in profit at WagerTalk among 33 cappers in 3 of the last 5 years and turned a profit in 5 of the last 6 seasons – no guessing here, just a consistent winning strategy and results. Your bankroll will thank you later, and your wallet will too with 50% off! (this includes MLB, KBO, Japanese, World Baseball Classic, Mexican baseball and EVERY other baseball bet all year)

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_______

Ballpark: Scottsdale Stadium (Giants’ spring home).
Probable starters: Valente Bellozo (COL) vs Blade Tidwell (SF).
Weather (Scottsdale): mostly sunny, ~82–88°F in the afternoon → dry/warm air = small HR/carry bump.

Assumptions

• Starters short: project 2.0 IP each (early-camp, quick hook).

• Bullpen + org depth matter most, especially innings 6–9.

• Farm/depth proxy: Giants #19, Rockies #28 in ESPN’s 2026 farm rankings → late innings skew more chaotic for COL.

Starters (baseline RA9 ≈ ERA)

• Tidwell RA9 = 4.48, Bellozo RA9 = 4.65.

Bullpens

• Rockies bullpen ERA: 5.18 (29th).

• Giants bullpen ERA: 3.48 (4th).

Projected full-game score: COL 4.5 — SF 6.7

Value chart

THE PLAY: Miami Marlins 194

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Why trust Tokyo Brandon? Because the record speaks for itself:

#1 MLB profit 2024 (+145 units)
#1 All-Sports Profit 2024 (+180u)
#1 All-Sports Profit 2022 (+125u)
#1 All-Sports Profit 2021 (+225u)
#4 All-Sports Profit 2025 (+49u)

Tokyo Brandon has finished #1 overall in profit at WagerTalk among 33 cappers in 3 of the last 5 years and turned a profit in 5 of the last 6 seasons – no guessing here, just a consistent winning strategy and results. Your bankroll will thank you later, and your wallet will too with 50% off! (this includes MLB, KBO, Japanese, World Baseball Classic, Mexican baseball and EVERY other baseball bet all year)

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_______
Spring Training — MIA @ TOR (Feb 26, 2026)

Park: TD Ballpark (Dunedin, FL) — historically played very hitter-friendly in prior data snapshots (not perfect for spring comps, but it nudges run environment up a bit).
Probable starters: Braxton Garrett (MIA) vs Kevin Gausman (TOR).
Weather: mostly sunny, warming into the low/mid-70s°F → small scoring bump.

Assumptions

• Starters short-leashed: project 2.0 IP each (early-camp).

• Bullpen + depth decide (innings 6–9 are the chaos innings).

• H2H batter-vs-pitcher since 4/15/2025: not meaningful here (Garrett missed 2025; spring lineups rotate constantly), so I don’t weight it as signal.

Bullpens (2025 baselines)

• MIA bullpen ERA: 4.28

• TOR bullpen ERA: 3.98

Projected full-game score (1 decimal): MIA 4.8 — TOR 5.6

Fair odds (full game) vs book

• TOR win% ≈ 57.7% → fair ML ≈ -136

• MIA win% ≈ 42.3% → fair ML ≈ +136

Value chart

Takeaway: Toronto should be favored, but -230 is a spring-training tax. With Garrett being managed carefully but the market still charging “big favorite” prices, MIA +190 grades as the value side in this model.

THE PLAY: New York Yankees -120

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Why trust Tokyo Brandon? Because the record speaks for itself:

#1 MLB profit 2024 (+145 units)
#1 All-Sports Profit 2024 (+180u)
#1 All-Sports Profit 2022 (+125u)
#1 All-Sports Profit 2021 (+225u)
#4 All-Sports Profit 2025 (+49u)

Tokyo Brandon has finished #1 overall in profit at WagerTalk among 33 cappers in 3 of the last 5 years and turned a profit in 5 of the last 6 seasons – no guessing here, just a consistent winning strategy and results. Your bankroll will thank you later, and your wallet will too with 50% off! (this includes MLB, KBO, Japanese, World Baseball Classic, Mexican baseball and EVERY other baseball bet all year)

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_______

Offense baseline

• Yankees: 5.24 R/G

• Giants: 4.35 R/G

• League run environment proxy: AL 4.27, NL 4.51

  • Offense index (approx):

    • NYY: 1.194

    • SFG: 0.991

Starters

Max Fried (NYY, LHP)

• 2025: 2.86 ERA; Away 3.28, Night 2.90

• 2024 vs SF: 5.1 IP, 3 ER, 3 BB, 5 K

Logan Webb (SFG, RHP)

• 2025 home/away: Home 3.10 ERA, Away 3.36 ERA

• 2025 day/night: Night 3.36 ERA

  batter-vs-pitcher H2H (since 2024)

• Fried vs Giants: 3 ER / 5.1 IP (tiny sample)

• Webb vs Yankees: 7 ER / 12.0 IP (tiny sample)

Projected lineups

• Yankees: Grisham, Judge, Bellinger, Rice, Stanton, Chisholm, McMahon, Caballero, Wells

• Giants: Ramos, Devers, Adames, Chapman, Jung Hoo Lee, Bader, Eldridge, Schmitt, Bailey

1) Starter expected innings (March workload)

Because this is late March and both pitchers have March samples that are short:

• Webb: 5.0 IP

• Fried: 5.1 IP

2) Build “game-context” RA9 for each starter

Fried context RA9

• Context base: average of Away (3.28) and Night (2.90) ⇒ 3.09

• Small March bump = 3.24

• H2H RA9 vs SF / Blend = 3.65

Webb context RA9

• Context base: average of Home (3.10) and Night (3.36) ⇒ 3.23

• Tiny March bump: = 3.29

• H2H RA9 vs NYY / Blend = 3.68

3) Park/weather/travel adjustments

• Travel penalty to NYY bats: I applied -3% to NYY run creation.

• Weather/park: mild cool-evening suppression factor (0.97) typical of SF night baseball. (This is an assumption, not a sourced fact.)

4) Convert to runs (starter + bullpen)

Bullpens: because “last 30 days” for late-March isn’t reliably anchorable today, generic bullpen strength assumptions (as the season progresses this will be clearer)

Projection results

Full game projected score

• NYY 4.0 — SFG 3.2

• Projected total: 7.2

1st 5 innings projected score

• NYY 2.2 — SFG 1.8

• 1st5 projected total: 4.0

Starting pitcher boxscore

Hitter boxscore projection

NYY hitters

SFG hitters

Wager & Probability Analysis

Moneyline value vs book

Totals value (Full game O/U 7)

Model total = 7.2 → very close to 7 (and 7 is a push number).

Totals value (1st 5 innings)

I don’t have a posted DK 1st5 line this far out, so I’m using the common pairing 1st5 O/U 3.5 (-110/-110) purely for comparison.

Pros for Yankees -120

• Price vs my fair line: -120 implies about 54.5% win probability. My model has NYY around 62% (fair about -164), so the number is cheap relative to the projection.

• Offensive ceiling edge: Even in a low-total environment, the Yankees’ lineup projects to create more “multi-run” innings (one big swing + traffic) than SF, which matters when totals are ~7 and every run is precious.

• Starter matchup isn’t a disadvantage: Webb is excellent at home, but Fried’s away/night profile + H2H slice doesn’t scream “avoid.” My projection has both starters around ~2 ER through ~5 innings, so the bet isn’t leaning on one pitcher melting down.

• Late-game leverage: In tight, low-scoring games, the better bullpen/high-K arms and pinch-hit depth tend to matter more. I generally rate NYY’s late-inning options as the side more likely to convert a 1-run edge into a win.

• 7 total = fewer “randomness runs”: Lower totals reduce the chance a weaker team wins via 9–7 chaos; games become more about pitching, defense, and a couple high-leverage ABs—areas that typically favor the higher-talent roster.