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Ohio harvest intel
TrophyBook Opportunity Score · v1

Ohio county Opportunity Score

One 0–100 number per county that ranks it against Ohio's other 87 counties on public harvest and record-book data. Higher means stronger numbers. It isn't the chance of taking a buck.

Harvest through the 2025-26 season · record-book entries 2019–2023

What goes into it

  • Total harvestDeer checked in the latest completed season. The fullest count of deer taken in each county, so it carries the most weight.
    40 pts
  • Harvest trendAverage harvest of the last 3 completed seasons vs the 3 before. Whether a county’s harvest is building or slipping over six seasons.
    25 pts
  • Harvest per sq miLatest completed season’s harvest divided by the county’s land area. Evens out county size: a small county can hold as many deer per square mile as a big one.
    20 pts
  • Book-buck oddsBuckeye Big Buck Club entries per 1,000 deer checked over five seasons, pulled toward the state rate when the sample is small. The trophy signal. It rests on far fewer records than harvest, so it carries the least.
    15 pts
v1 · all 88 counties

The ranking

Counties with the same score share a rank. Each county page shows its full breakdown.

RankCountyScoreTotal harvestHarvest trendHarvest per sq miBook-buck odds
#1Jefferson853,691 deer
#18
+44%
#5
9.0 per sq mi
#13
3.3 per 1,000 deer
#16
#2Carroll806,077 deer
#6
+28%
#17
15.4 per sq mi
#1
1.5 per 1,000 deer
#68
#3Harrison794,959 deer
#13
+31%
#12
12.3 per sq mi
#6
1.7 per 1,000 deer
#62
T4Ashland765,108 deer
#12
+22%
#30
12.1 per sq mi
#7
2.1 per 1,000 deer
#46
T4Belmont763,725 deer
#17
+28%
#17
6.9 per sq mi
#22
2.5 per 1,000 deer
#36
T4Columbiana765,245 deer
#10
+28%
#17
9.9 per sq mi
#10
1.4 per 1,000 deer
#75
T4Huron763,520 deer
#20
+26%
#23
7.2 per sq mi
#20
2.8 per 1,000 deer
#27
T8Morrow742,767 deer
#33
+30%
#13
6.8 per sq mi
#23
3.8 per 1,000 deer
#9
T8Tuscarawas747,623 deer
#2
+13%
#51
13.4 per sq mi
#4
1.8 per 1,000 deer
#56
T10Knox736,704 deer
#4
+19%
#36
12.8 per sq mi
#5
1.3 per 1,000 deer
#77
T10Richland735,151 deer
#11
+23%
#28
10.4 per sq mi
#8
1.5 per 1,000 deer
#68
#12Muskingum726,283 deer
#5
+11%
#56
9.5 per sq mi
#11
2.2 per 1,000 deer
#44
#13Coshocton718,527 deer
#1
+14%
#50
15.1 per sq mi
#2
0.9 per 1,000 deer
#83
#14Ashtabula707,161 deer
#3
+16%
#43
10.2 per sq mi
#9
0.9 per 1,000 deer
#83
#15Trumbull695,324 deer
#9
+21%
#33
8.6 per sq mi
#14
0.8 per 1,000 deer
#87
T16Geauga673,215 deer
#24
+27%
#21
8.0 per sq mi
#16
1.3 per 1,000 deer
#77
T16Licking675,770 deer
#7
+4%
#70
8.5 per sq mi
#15
2.4 per 1,000 deer
#38
T16Stark674,466 deer
#15
+21%
#33
7.8 per sq mi
#17
1.3 per 1,000 deer
#77
T19Holmes665,717 deer
#8
+12%
#54
13.5 per sq mi
#3
0.9 per 1,000 deer
#83
T19Seneca663,017 deer
#27
+30%
#13
5.5 per sq mi
#38
2.0 per 1,000 deer
#51
T19Williams662,842 deer
#30
+27%
#21
6.7 per sq mi
#24
1.9 per 1,000 deer
#55
T22Hancock652,345 deer
#41
+30%
#13
4.4 per sq mi
#50
4.8 per 1,000 deer
#2
T22Portage653,498 deer
#21
+19%
#36
7.2 per sq mi
#20
1.8 per 1,000 deer
#56
T24Mahoning642,768 deer
#32
+24%
#25
6.7 per sq mi
#24
2.1 per 1,000 deer
#46
T24Wayne643,562 deer
#19
+24%
#25
6.4 per sq mi
#27
1.2 per 1,000 deer
#81
T26Gallia632,956 deer
#29
+10%
#57
6.3 per sq mi
#28
3.7 per 1,000 deer
#11
T26Perry632,989 deer
#28
+8%
#61
7.3 per sq mi
#19
3.4 per 1,000 deer
#13
#28Hardin622,053 deer
#45
+28%
#17
4.4 per sq mi
#50
4.4 per 1,000 deer
#3
T29Adams613,895 deer
#16
+15%
#45
6.7 per sq mi
#24
1.5 per 1,000 deer
#68
T29Allen611,831 deer
#52
+46%
#3
4.5 per sq mi
#49
3.0 per 1,000 deer
#21
T31Guernsey594,804 deer
#14
+5%
#66
9.2 per sq mi
#12
1.5 per 1,000 deer
#68
T31Lorain592,799 deer
#31
+17%
#40
5.7 per sq mi
#36
2.3 per 1,000 deer
#40
T31Ross593,402 deer
#22
+5%
#66
4.9 per sq mi
#40
3.2 per 1,000 deer
#19
T34Medina573,133 deer
#25
+9%
#58
7.4 per sq mi
#18
1.8 per 1,000 deer
#56
T34Wyandot572,405 deer
#39
+19%
#36
5.9 per sq mi
#34
2.4 per 1,000 deer
#38
T36Auglaize551,621 deer
#54
+30%
#13
4.0 per sq mi
#55
2.9 per 1,000 deer
#25
T36Crawford551,881 deer
#49
+23%
#28
4.7 per sq mi
#43
2.7 per 1,000 deer
#29
T36Highland553,389 deer
#23
+12%
#54
6.1 per sq mi
#32
1.5 per 1,000 deer
#68
#39Fulton531,489 deer
#57
+36%
#7
3.7 per sq mi
#57
2.6 per 1,000 deer
#32
#40Logan522,712 deer
#35
+15%
#45
5.9 per sq mi
#34
1.7 per 1,000 deer
#62
T41Fairfield512,351 deer
#40
+5%
#66
4.7 per sq mi
#43
4.0 per 1,000 deer
#8
T41Mercer511,432 deer
#61
+34%
#8
3.1 per sq mi
#66
3.0 per 1,000 deer
#21
T43Sandusky501,485 deer
#58
+22%
#30
3.6 per sq mi
#60
3.8 per 1,000 deer
#9
T43Shelby501,562 deer
#55
+24%
#25
3.8 per sq mi
#56
2.7 per 1,000 deer
#29
T45Brown483,023 deer
#26
+6%
#65
6.2 per sq mi
#31
0.8 per 1,000 deer
#87
T45Marion481,384 deer
#63
+25%
#24
3.4 per sq mi
#62
3.1 per 1,000 deer
#20
T47Clinton471,173 deer
#71
+46%
#3
2.9 per sq mi
#69
2.8 per 1,000 deer
#27
T47Hocking472,643 deer
#36
-3%
#75
6.3 per sq mi
#28
2.1 per 1,000 deer
#46
T49Darke461,356 deer
#66
+32%
#11
2.3 per sq mi
#79
3.3 per 1,000 deer
#16
T49Monroe462,195 deer
#43
+8%
#61
4.8 per sq mi
#42
2.3 per 1,000 deer
#40
T49Pickaway461,153 deer
#73
+49%
#2
2.3 per sq mi
#79
3.4 per 1,000 deer
#13
T52Champaign451,867 deer
#51
+20%
#35
4.4 per sq mi
#50
1.8 per 1,000 deer
#56
T52Putnam451,376 deer
#65
+34%
#8
2.9 per sq mi
#69
2.3 per 1,000 deer
#40
T54Delaware441,877 deer
#50
+8%
#61
4.2 per sq mi
#53
3.0 per 1,000 deer
#21
T54Jackson442,643 deer
#36
-3%
#75
6.3 per sq mi
#28
1.7 per 1,000 deer
#62
T54Van Wert44968 deer
#79
+40%
#6
2.4 per sq mi
#77
4.2 per 1,000 deer
#4
#57Defiance431,887 deer
#48
-6%
#79
4.6 per sq mi
#47
3.6 per 1,000 deer
#12
T58Clermont422,759 deer
#34
0%
#74
6.1 per sq mi
#32
0.9 per 1,000 deer
#83
T58Scioto422,576 deer
#38
+9%
#58
4.2 per sq mi
#53
1.7 per 1,000 deer
#62
#60Wood411,434 deer
#60
+22%
#30
2.3 per sq mi
#79
2.9 per 1,000 deer
#25
T61Fayette40591 deer
#88
+50%
#1
1.5 per sq mi
#86
5.0 per 1,000 deer
#1
T61Pike402,156 deer
#44
+7%
#64
4.9 per sq mi
#40
1.3 per 1,000 deer
#77
T61Union401,401 deer
#62
+19%
#36
3.2 per sq mi
#64
2.5 per 1,000 deer
#36
T64Henry391,181 deer
#70
+17%
#40
2.8 per sq mi
#71
4.1 per 1,000 deer
#6
T64Lake391,245 deer
#69
+15%
#45
5.5 per sq mi
#38
2.3 per 1,000 deer
#40
T64Summit391,922 deer
#47
+2%
#73
4.7 per sq mi
#43
2.0 per 1,000 deer
#51
#67Noble372,260 deer
#42
-4%
#78
5.7 per sq mi
#36
1.1 per 1,000 deer
#82
#68Vinton361,931 deer
#46
-11%
#82
4.7 per sq mi
#43
2.0 per 1,000 deer
#51
#69Ottawa35851 deer
#83
+33%
#10
3.3 per sq mi
#63
1.8 per 1,000 deer
#56
#70Erie341,161 deer
#72
+16%
#43
4.6 per sq mi
#47
1.8 per 1,000 deer
#56
T71Lucas32965 deer
#80
+15%
#45
2.8 per sq mi
#71
4.1 per 1,000 deer
#6
T71Miami321,090 deer
#74
+17%
#40
2.7 per sq mi
#73
2.6 per 1,000 deer
#32
T71Morgan321,529 deer
#56
-11%
#82
3.7 per sq mi
#57
2.6 per 1,000 deer
#32
#74Lawrence311,682 deer
#53
+3%
#72
3.7 per sq mi
#57
1.6 per 1,000 deer
#67
T75Paulding291,286 deer
#68
-3%
#75
3.1 per sq mi
#66
3.3 per 1,000 deer
#16
T75Warren291,377 deer
#64
+5%
#66
3.5 per sq mi
#61
2.0 per 1,000 deer
#51
#77Preble271,354 deer
#67
+13%
#51
3.2 per sq mi
#64
1.4 per 1,000 deer
#75
#78Clark25954 deer
#81
+13%
#51
2.4 per sq mi
#77
2.7 per 1,000 deer
#29
#79Butler221,466 deer
#59
-7%
#80
3.1 per sq mi
#66
1.5 per 1,000 deer
#68
#80Greene211,076 deer
#75
+9%
#58
2.6 per sq mi
#74
1.7 per 1,000 deer
#62
#81Madison20767 deer
#86
+15%
#45
1.6 per sq mi
#85
2.2 per 1,000 deer
#44
#82Meigs181,060 deer
#76
-24%
#87
2.5 per sq mi
#76
2.6 per 1,000 deer
#32
#83Montgomery17980 deer
#78
+4%
#70
2.1 per sq mi
#82
2.1 per 1,000 deer
#46
T84Athens16917 deer
#82
-25%
#88
1.8 per sq mi
#83
3.4 per 1,000 deer
#13
T84Franklin16709 deer
#87
-15%
#85
1.3 per sq mi
#87
4.2 per 1,000 deer
#4
#86Hamilton151,058 deer
#77
-16%
#86
2.6 per sq mi
#74
2.1 per 1,000 deer
#46
#87Washington14783 deer
#85
-14%
#84
1.2 per sq mi
#88
3.0 per 1,000 deer
#21
#88Cuyahoga7822 deer
#84
-10%
#81
1.8 per sq mi
#83
1.5 per 1,000 deer
#68

How it's calculated

  1. For each input, every county gets a percentile: the share of the other 87 counties it's ahead of. Ties don't count as ahead.
  2. Each percentile is multiplied by its input's weight and divided by 100, giving points.
  3. The four point values are added and rounded. A county ahead of every other county on every input would score 100.

Jefferson, the current leader: 80% × 40 + 95% × 25 + 86% × 20 + 80% × 15 = 84.95, which rounds to 85.

Why percentiles

The inputs sit on very different scales: harvest is counted in hundreds or thousands of deer (last season, 591 to 8,527), book odds in single digits. Added as they are, harvest would drown out the rest. Percentiles put every input on the same 0–100 footing, and one extreme county can't stretch the scale for everyone else.

Where the numbers come from

  • Harvest, trend and harvest per square mile: ODNR's weekly deer harvest reports. The last report of each completed season stands in for its final total. Land area is from the U.S. Census.
  • Book-buck odds: the Buckeye Big Buck Club record book (2,141 entries from 2019–2023), per 1,000 deer checked in the same seasons. Small counties are pulled toward the state rate. How the odds are figured.
  • The score is recalculated from this stored data. It changes when a deer season ends and when the record book copy is refreshed, not week to week.

What it leaves out, and why

  • This season so far. Early weekly totals swing with weather and the calendar. Season to date is on each county page and the weekly view.
  • Deer population, public land and habitat. TrophyBook has no official county-by-county figures for these yet, and the score won't estimate them.
  • Weather. It describes a day, not a county. The sit forecast is a separate layer on the heat map.
  • Biggest book buck and number of entries. One deer sets the first; the second mostly repeats harvest.

Limits

  • It compares counties with each other. It says nothing about a particular property, and a low score doesn't mean a county can't produce a big buck.
  • Harvest counts hunters as much as deer: busy counties score higher partly because more people hunt them.
  • Disease years, like the 2025 EHD outbreak, pull down harvest and trend in the counties hit.
  • Record-book entries depend on hunters entering their bucks. They arrive late (racks dry before scoring, then the club processes them), so the odds leave out the two most recent seasons.

Versions

Any change to the inputs or weights gets a new version number, listed here.

VersionDateChange
v12026-10-04First version: total harvest 40, harvest trend 25, harvest per sq mi 20, book-buck odds 15.

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Ohio county Opportunity Score — TrophyBook