How MLB home runs actually behave

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Across 372 MLB players with at least 10 logged games in home runs, this page measures 27609 games in total. The median player in that field averages 0.1 home runs. The line closest to a coin flip sits at 0.5 for the median of the 372 players whose distribution supports one — half the field is more often over it, half more often under. The deepest sample here is Max Muncy, with 153 logged games. 372 of them have at least three games home and away, so the venue gap below is measured rather than assumed. Every figure is counted from official box scores and describes games already played. It is not a projection, and on its own it says nothing about whether any price on offer is worth taking.

Measured as of 2026-09-20, rebuilt daily from official box scores. Window: 2026-05-23 to 2026-09-20. No prices appear on this page.

Where the coin-flip line sits in MLB home runs

Each player's own most balanced threshold — the half-point line he has finished above in closest to half his logged games — measured from his distribution rather than read off a book. Deepest sample first, not best to worst. The 40 deepest samples of 372 measured; the figures above this table are over all 372.

MLB home runs — measured field
Player	Club	Logged games	Average	Balanced line	Hit rate there
Max Muncy	Dodgers	153	0.2 home runs	Over 0.5	23 of 153 (15%)
Alec Burleson	Cardinals	107	0.1 home runs	Over 0.5	14 of 107 (13%)
Iván Herrera	Cardinals	107	0.1 home runs	Over 0.5	9 of 107 (8%)
Junior Caminero	Rays	107	0.3 home runs	Over 0.5	25 of 107 (23%)
Sal Stewart	Reds	107	0.2 home runs	Over 0.5	19 of 107 (18%)
Bryce Harper	Phillies	106	0.2 home runs	Over 0.5	16 of 106 (15%)
Jackson Merrill	Padres	106	0.2 home runs	Over 0.5	20 of 106 (19%)
Miguel Vargas	White Sox	106	0.2 home runs	Over 0.5	21 of 106 (20%)
Pete Crow-Armstrong	Cubs	106	0.4 home runs	Over 0.5	30 of 106 (28%)
Bryan Reynolds	Pirates	105	0.1 home runs	Over 0.5	12 of 105 (11%)
Ezequiel Duran	Rangers	105	0.1 home runs	Over 0.5	12 of 105 (11%)
Fernando Tatis Jr.	Padres	105	0.2 home runs	Over 0.5	20 of 105 (19%)
Jackson Chourio	Brewers	105	0.2 home runs	Over 0.5	20 of 105 (19%)
JJ Bleday	Reds	105	0.2 home runs	Over 0.5	16 of 105 (15%)
Jordan Walker	Cardinals	105	0.1 home runs	Over 0.5	15 of 105 (14%)
Matt Olson	Braves	105	0.2 home runs	Over 0.5	23 of 105 (22%)
Ozzie Albies	Braves	105	0.1 home runs	Over 0.5	13 of 105 (12%)
Bryson Stott	Phillies	104	0.0 home runs	Over 0.5	5 of 104 (5%)
Corbin Carroll	Diamondbacks	104	0.1 home runs	Over 0.5	15 of 104 (14%)
Geraldo Perdomo	Diamondbacks	104	0.1 home runs	Over 0.5	7 of 104 (7%)
Henry Bolte	Athletics	104	0.1 home runs	Over 0.5	10 of 104 (10%)
Manny Machado	Padres	104	0.2 home runs	Over 0.5	19 of 104 (18%)
Michael Busch	Cubs	104	0.2 home runs	Over 0.5	16 of 104 (15%)
Pete Alonso	Orioles	104	0.3 home runs	Over 0.5	27 of 104 (26%)
Yandy Díaz	Rays	104	0.1 home runs	Over 0.5	13 of 104 (13%)
Alex Bregman	Cubs	103	0.2 home runs	Over 0.5	17 of 103 (17%)
Bo Bichette	Mets	103	0.1 home runs	Over 0.5	10 of 103 (10%)
Brandon Marsh	Phillies	103	0.1 home runs	Over 0.5	12 of 103 (12%)
Brooks Lee	Twins	103	0.2 home runs	Over 0.5	17 of 103 (17%)
Daylen Lile	Nationals	103	0.1 home runs	Over 0.5	12 of 103 (12%)
Ernie Clement	Blue Jays	103	0.1 home runs	Over 0.5	6 of 103 (6%)
Gunnar Henderson	Orioles	103	0.1 home runs	Over 0.5	11 of 103 (11%)
Isaac Paredes	Astros	103	0.2 home runs	Over 0.5	17 of 103 (17%)
Kyle Schwarber	Phillies	103	0.2 home runs	Over 0.5	21 of 103 (20%)
Nico Hoerner	Cubs	103	0.0 home runs	Over 0.5	4 of 103 (4%)
Tristan Peters	White Sox	103	0.1 home runs	Over 0.5	10 of 103 (10%)
A.J. Ewing	Mets	102	0.1 home runs	Over 0.5	9 of 102 (9%)
Alec Bohm	Phillies	102	0.1 home runs	Over 0.5	10 of 102 (10%)
Ben Rice	Yankees	102	0.2 home runs	Over 0.5	22 of 102 (22%)
Caleb Durbin	Red Sox	102	0.1 home runs	Over 0.5	11 of 102 (11%)

The widest home and away gaps in MLB home runs

Both averages are over the games actually played at each venue, and the game count for each is in the cell beside it. A gap measured over a handful of games either way is one you cannot distinguish from noise — this table prints the denominators so you can tell which is which. Three games a side is the floor for appearing here at all.

MLB home runs — venue gaps
Player	Club	Home (games)	Away (games)	Gap
Eduardo Valencia	Tigers	0.4 (16)	0.0 (20)	0.4 at home
Adael Amador	Rockies	0.1 (15)	0.4 (7)	0.3 away
Pete Crow-Armstrong	Cubs	0.5 (52)	0.2 (54)	0.3 at home
Danny Jansen	Rangers	0.0 (17)	0.3 (11)	0.3 away
Will Smith	Dodgers	0.3 (17)	0.0 (9)	0.3 at home
Ezequiel Duran	Rangers	0.2 (56)	0.0 (49)	0.2 at home
Tristan Peters	White Sox	0.2 (50)	0.0 (53)	0.2 at home
Ian Happ	Cubs	0.0 (48)	0.2 (53)	0.2 away
Salvador Perez	Royals	0.2 (44)	0.0 (53)	0.2 at home
Garrett Mitchell	Brewers	0.2 (49)	0.0 (47)	0.2 at home

Ahead of their own baseline over the last five

Last five logged games against the full log, reported only where the gap is more than a tenth of the player’s own average. It is a description of five games that have been played and it is not a forecast of the sixth.

Ahead of their own baseline over the last five
Player	Club	Last 5	Full log	Difference
Ryan Kreidler	Twins	0.4 home runs	0.1 over 89	612% above
Jeremiah Jackson	Orioles	0.4 home runs	0.1 over 50	567% above
Miguel Rojas	Dodgers	0.2 home runs	0.0 over 72	380% above
Agustín Ramírez	Marlins	0.4 home runs	0.1 over 34	353% above
Travis Bazzana	Guardians	0.6 home runs	0.1 over 97	348% above
Nick Allen	Astros	0.2 home runs	0.0 over 67	347% above
Pavin Smith	Diamondbacks	0.4 home runs	0.1 over 33	340% above
Jonathan Aranda	Rays	0.6 home runs	0.1 over 100	329% above
Xander Bogaerts	Padres	0.2 home runs	0.1 over 99	296% above
Blaze Jordan	Cardinals	0.2 home runs	0.1 over 58	287% above

Behind their own baseline over the last five

The same comparison in the other direction, and the same caveat: five games is five games, and a run below a baseline is as often noise as it is a change.

Behind their own baseline over the last five
Player	Club	Last 5	Full log	Difference
Junior Caminero	Rays	0.0 home runs	0.3 over 107	100% below
Iván Herrera	Cardinals	0.0 home runs	0.1 over 107	100% below
Bryce Harper	Phillies	0.0 home runs	0.2 over 106	100% below
Ezequiel Duran	Rangers	0.0 home runs	0.1 over 105	100% below
Jordan Walker	Cardinals	0.0 home runs	0.1 over 105	100% below
Bryan Reynolds	Pirates	0.0 home runs	0.1 over 105	100% below
JJ Bleday	Reds	0.0 home runs	0.2 over 105	100% below
Yandy Díaz	Rays	0.0 home runs	0.1 over 104	100% below
Manny Machado	Padres	0.0 home runs	0.2 over 104	100% below
Bryson Stott	Phillies	0.0 home runs	0.0 over 104	100% below

Common questions

Where does the MLB home runs coin-flip line sit?

At the half-point threshold each player has finished above in about half their logged games. It is measured per player from their own distribution rather than read off a sportsbook, which is why it is on this page at all: a posted line moves through the day and this page is rebuilt once, so a number taken from a book would be wrong by morning.

Is this a ranking of the best MLB home runs props?

No. Nothing here is ordered by how good a bet it is, and no price is quoted anywhere on the page. The tables are ordered by sample depth, by spread and by the size of a measured gap, because those are the things a game log can actually establish.

How many games are behind each home runs figure?

The count is printed beside every one of them. A player only appears once they have ten or more logged games in the market, and games they did not play are absent rather than counted as zero, so an average describes games they were actually in.

Does a home runs trend on this page predict the next game?

No. A trend here is a comparison between a player’s last five logged games and their full log, and it is reported only when the gap is large enough to be worth naming. It is a description of what has happened, and history is not a forecast.

What these tables are measuring, explained: how to read a hit rate and what its denominator hides, what an over/under line is and what it is not.

MLB player props and live odds

Odds and lines come from licensed data feeds and refresh on a schedule; hit rates, edges and grades are Parlay Builder’s own calculations.