Detroit Tigers vs Toronto Blue Jays Betting Odds & Game Analysis | June 10


By Claudio Fortuna | June 10, 2022 12:13 PM

The Toronto Blue Jays visit the Detroit Tigers at Comerica Park, taking the field at 7:10pm ET on Friday June 10th, 7:10pm ET. The Detroit Tigers are 1.5-run underdogs in the game, and the Over/Under is set at 9 runs.

Welcome to the BestOdds betting breakdown where we identify performance trends in order to analyze teams’ chances in the upcoming game.

Today, we are looking at the Major League Baseball game between the Toronto Blue Jays and Detroit Tigers.

Detroit and Toronto Betting Info

  • The Blue Jays have won seven of their last eight road games.
  • Each of the last seven games between the Blue Jays and Tigers have gone UNDER the total runs line.

Toronto Blue Jays Stats

Season Stats
Team Batting AVG .249
Team OBP .321
AVG Runs Scored 4.4
Team ERA 3.71
Team WHIP 1.22
Starters ERA 3.59
Starters WHIP 1.21

Key Players for Toronto

  • Jordan Romano: 2.95 ERA, 10.97 K/9, 1.22 WHIP
  • Yusei Kikuchi: 4.44 ERA, 10.03 K/9, 1.48 WHIP
  • Matthew Gage: 0.00 ERA, 10.80 K/9, 0.60 WHIP
  • Bo Bichette: .262 Batting Avg, .312 OBP, .438 SLG, .750 OPS, 30 Runs
  • Lourdes Gurriel Jr.: .253 Batting Avg, .315 OBP, .348 SLG, .663 OPS, 17 Runs
  • Daniel Jansen: .232 Batting Avg, .290 OBP, .625 SLG, .915 OPS, 9 Runs
  • Teoscar Hernández: .223 Batting Avg, .288 OBP, .372 SLG, .660 OPS, 13 Runs
  • George Springer: .271 Batting Avg, .342 OBP, .510 SLG, .852 OPS, 34 Runs
  • Alejandro Kirk: .322 Batting Avg, .401 OBP, .477 SLG, .878 OPS, 23 Runs
  • Santiago Espinal: .290 Batting Avg, .346 OBP, .451 SLG, .797 OPS, 21 Runs
  • Vladimir Guerrero Jr.: .243 Batting Avg, .335 OBP, .465 SLG, .800 OPS, 26 Runs
  • Matt Chapman: .222 Batting Avg, .312 OBP, .381 SLG, .693 OPS, 27 Runs

Detroit Tigers Stats

Season Stats
Team Batting AVG .221
Team OBP .279
AVG Runs Scored 2.8
Team ERA 3.61
Team WHIP 1.17
Starters ERA 4.26
Starters WHIP 1.23

Key Players for Detroit

  • Gregory Soto: 1.71 ERA, 7.71 K/9, 1.14 WHIP
  • Jason Foley: 2.70 ERA, 4.32 K/9, 1.14 WHIP
  • Rony García: 4.50 ERA, 10.61 K/9, 1.00 WHIP
  • Jonathan Schoop: .198 Batting Avg, .237 OBP, .329 SLG, .566 OPS, 17 Runs
  • Tucker Barnhart: .237 Batting Avg, .274 OBP, .271 SLG, .545 OPS, 5 Runs
  • Ednel Báez: .200 Batting Avg, .235 OBP, .309 SLG, .544 OPS, 12 Runs
  • Austin Meadows: .269 Batting Avg, .363 OBP, .352 SLG, .715 OPS, 9 Runs
  • Willi Castro: .258 Batting Avg, .300 OBP, .333 SLG, .633 OPS, 13 Runs
  • Harold Castro: .281 Batting Avg, .297 OBP, .456 SLG, .753 OPS, 14 Runs
  • Jose Cabrera: .297 Batting Avg, .339 OBP, .384 SLG, .723 OPS, 14 Runs
  • Derek Hill: .225 Batting Avg, .267 OBP, .288 SLG, .555 OPS, 8 Runs
  • Spencer Torkelson: .190 Batting Avg, .295 OBP, .310 SLG, .605 OPS, 12 Runs

MLB Computer Picks For Betting Previews

MLB computer picks are betting recommendations made by an AI computer algorithm with a final check by sports betting analyst.

The AI algorithm considers various data points to come up with its betting prediction. Betting trends, game venue, line-ups, weather, injury, news updates, and a wide range of stats are the factors taken into account when making MLB betting preview computer picks.

A professional sports bettor performs the final check.

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