{"id":29822,"date":"2025-10-28T10:58:13","date_gmt":"2025-10-28T10:58:13","guid":{"rendered":"https:\/\/atswins.ai\/blog\/?p=29822"},"modified":"2025-10-28T10:58:13","modified_gmt":"2025-10-28T10:58:13","slug":"knicks-aim-to-silence-milwaukees-home-court-edge-at-fiserv-forum","status":"publish","type":"post","link":"https:\/\/atswins.ai\/blog\/knicks-aim-to-silence-milwaukees-home-court-edge-at-fiserv-forum\/","title":{"rendered":"Knicks Aim to Silence Milwaukee\u2019s Home Court Edge at Fiserv Forum"},"content":{"rendered":"<h1 data-start=\"204\" data-end=\"256\">1) Which models I checked (top\/ reputable sources)<\/h1>\n<ul data-start=\"257\" data-end=\"887\">\n<li data-start=\"257\" data-end=\"375\">\n<p data-start=\"259\" data-end=\"375\">BetQL (paywalled model; site shows they have a simulation-based projection).<\/p>\n<\/li>\n<li data-start=\"376\" data-end=\"485\">\n<p data-start=\"378\" data-end=\"485\">ESPN (BPI \/ Matchup Predictor \u2014 gives win probability and context).<\/p>\n<\/li>\n<li data-start=\"486\" data-end=\"616\">\n<p data-start=\"488\" data-end=\"616\">SportsLine (Proven Projection Model \/ SportsLine consensus referenced in their preview).<\/p>\n<\/li>\n<li data-start=\"617\" data-end=\"722\">\n<p data-start=\"619\" data-end=\"722\">Dimers (provides a simulation-based <strong data-start=\"655\" data-end=\"680\">projected final score<\/strong>).<\/p>\n<\/li>\n<li data-start=\"723\" data-end=\"887\">\n<p data-start=\"725\" data-end=\"887\">OddsShark \/ FoxSports \/ WinComparator (used as additional published model\/computer predictions and market-trend references).<\/p>\n<\/li>\n<\/ul>\n<blockquote data-start=\"889\" data-end=\"1244\">\n<p data-start=\"891\" data-end=\"1244\">Note: BetQL and SportsLine often require subscriptions for their exact per-game score outputs; when paywalled I used the public summaries and other freely-available model outputs (Dimers, OddsShark, FoxSports) to build the averaged \u201cpublished score\u201d set. I\u2019ve flagged sources so you can inspect any paywalled items.<\/p>\n<\/blockquote>\n<hr data-start=\"1246\" data-end=\"1249\" \/>\n<h1 data-start=\"1251\" data-end=\"1306\">2) Published model score predictions I could retrieve<\/h1>\n<p data-start=\"1307\" data-end=\"1380\">(only models that published numeric <strong data-start=\"1343\" data-end=\"1358\">final-score<\/strong> projections publicly)<\/p>\n<ol data-start=\"1382\" data-end=\"1692\">\n<li data-start=\"1382\" data-end=\"1464\">\n<p data-start=\"1385\" data-end=\"1464\">FoxSports \u2014 <strong data-start=\"1397\" data-end=\"1423\">Knicks 116 \u2014 Bucks 113<\/strong>.<\/p>\n<\/li>\n<li data-start=\"1465\" data-end=\"1544\">\n<p data-start=\"1468\" data-end=\"1544\">Dimers \u2014 <strong data-start=\"1477\" data-end=\"1503\">Knicks 116 \u2014 Bucks 114<\/strong>.<\/p>\n<\/li>\n<li data-start=\"1545\" data-end=\"1692\">\n<p data-start=\"1548\" data-end=\"1692\">OddsShark (computer pick) \u2014 Knicks <strong data-start=\"1583\" data-end=\"1592\">114.0<\/strong> \/ Bucks <strong data-start=\"1601\" data-end=\"1610\">115.4<\/strong> (i.e., roughly <strong data-start=\"1626\" data-end=\"1652\">Knicks 114 \u2014 Bucks 115<\/strong>).<\/p>\n<\/li>\n<\/ol>\n<p data-start=\"1694\" data-end=\"1943\">(Several other top models (BetQL, SportsLine, ESPN BPI) provide win probabilities, edges and paywalled projections rather than a freely-published exact final-score; I used those for probabilities\/context below.)<\/p>\n<hr data-start=\"1945\" data-end=\"1948\" \/>\n<h1 data-start=\"1950\" data-end=\"2023\">3) Average of the available final-score projections (calculation shown)<\/h1>\n<p data-start=\"2024\" data-end=\"2111\">We have three numeric score predictions (Knicks: 116, 116, 114 \u2014 Bucks: 113, 114, 115).<\/p>\n<p data-start=\"2113\" data-end=\"2139\">Digit-by-digit arithmetic:<\/p>\n<ul data-start=\"2141\" data-end=\"2302\">\n<li data-start=\"2141\" data-end=\"2238\">\n<p data-start=\"2143\" data-end=\"2238\">Knicks total = 116 + 116 + 114 = 346.<br data-start=\"2180\" data-end=\"2183\" \/>346 \u00f7 3 = 115.333&#8230; \u2192 <strong data-start=\"2208\" data-end=\"2218\">115.33<\/strong> (rounded to 115).<\/p>\n<\/li>\n<li data-start=\"2239\" data-end=\"2302\">\n<p data-start=\"2241\" data-end=\"2302\">Bucks total = 113 + 114 + 115 = 342.<br data-start=\"2277\" data-end=\"2280\" \/>342 \u00f7 3 = <strong data-start=\"2292\" data-end=\"2301\">114.0<\/strong>.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"2304\" data-end=\"2404\"><strong data-start=\"2304\" data-end=\"2358\">Averaged (published-model) final-score prediction:<\/strong> <strong data-start=\"2359\" data-end=\"2385\">Knicks 115 \u2014 Bucks 114<\/strong> (total \u2248 <strong data-start=\"2395\" data-end=\"2402\">229<\/strong>).<\/p>\n<p data-start=\"2406\" data-end=\"2517\">Sources for the inputs used in averaging: Dimers, FoxSports, OddsShark.<\/p>\n<hr data-start=\"2519\" data-end=\"2522\" \/>\n<h1 data-start=\"2524\" data-end=\"2572\">4) My independent prediction (method + result)<\/h1>\n<h3 data-start=\"2574\" data-end=\"2591\">Inputs I used<\/h3>\n<ul data-start=\"2592\" data-end=\"3878\">\n<li data-start=\"2592\" data-end=\"2898\">\n<p data-start=\"2594\" data-end=\"2898\"><strong data-start=\"2594\" data-end=\"2621\">Pythagorean expectation<\/strong>: used league offensive\/defensive points trends and recent scoring to form expected points \u2014 adjusted for opponent defense. (I used team season scoring rates in the public previews: ESPN\/NBA game charts for offensive numbers and pace.)<\/p>\n<\/li>\n<li data-start=\"2899\" data-end=\"3121\">\n<p data-start=\"2901\" data-end=\"3121\"><strong data-start=\"2901\" data-end=\"2931\">Strength of Schedule (SOS)<\/strong>: ESPN BPI \/ Matchup Predictor accounts for SOS and location (ESPN\u2019s Matchup Predictor shows nearly even odds). I leaned on that to temper extremes.<\/p>\n<\/li>\n<li data-start=\"3122\" data-end=\"3667\">\n<p data-start=\"3124\" data-end=\"3152\"><strong data-start=\"3124\" data-end=\"3151\">External factors \/ news<\/strong>:<\/p>\n<ul data-start=\"3155\" data-end=\"3667\">\n<li data-start=\"3155\" data-end=\"3457\">\n<p data-start=\"3157\" data-end=\"3457\"><strong data-start=\"3157\" data-end=\"3184\">Injuries \/ availability<\/strong>: Mitchell Robinson listed <strong data-start=\"3211\" data-end=\"3218\">OUT<\/strong> (affects Knicks rim protection &amp; rebounding). Josh Hart is reported as still working back and &#8220;questionable&#8221;\/limited recently \u2014 that affects Knicks&#8217; bench\/energy. (NY Post \/ local injury reports).<\/p>\n<\/li>\n<li data-start=\"3460\" data-end=\"3667\">\n<p data-start=\"3462\" data-end=\"3667\">Bucks form: Giannis had a 40-point game recently and the Bucks have played well at home; Action Network and ESPN preview show Bucks home trends and recent results.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li data-start=\"3668\" data-end=\"3878\">\n<p data-start=\"3670\" data-end=\"3878\"><strong data-start=\"3670\" data-end=\"3699\">Recent performance trends<\/strong>: both teams 2-1, Knicks have recent head-to-head success vs Bucks; many services show Knicks have won recent meetings (head-to-head edge).<\/p>\n<\/li>\n<\/ul>\n<h3 data-start=\"3880\" data-end=\"3944\">My Pythagorean\/SOS-adjusted projection (calculation summary)<\/h3>\n<ul data-start=\"3945\" data-end=\"4396\">\n<li data-start=\"3945\" data-end=\"4143\">\n<p data-start=\"3947\" data-end=\"4143\">Base offensive estimate (league\/season rates plus game location + opponent defense) -&gt; Knicks ~ <strong data-start=\"4043\" data-end=\"4054\">113\u2013116<\/strong> expected points; Bucks ~ <strong data-start=\"4080\" data-end=\"4091\">113\u2013116<\/strong> depending on rotations and whether Robinson sits.<\/p>\n<\/li>\n<li data-start=\"4144\" data-end=\"4325\">\n<p data-start=\"4146\" data-end=\"4325\">Adjust for Robinson OUT (Knicks defense\/board downside \u2192 cost ~1\u20132 pts for Knicks on defense, but Knicks still have perimeter defense from Anunoby\/Brunson limiting Bucks\u2019 pace).<\/p>\n<\/li>\n<li data-start=\"4326\" data-end=\"4396\">\n<p data-start=\"4328\" data-end=\"4396\">Adjust for Giannis hot form (should push Bucks scoring up ~1\u20132 pts).<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"4398\" data-end=\"4484\"><strong data-start=\"4398\" data-end=\"4440\">My independent final-score prediction:<\/strong> <strong data-start=\"4441\" data-end=\"4467\">Knicks 114 \u2014 Bucks 113<\/strong> (total <strong data-start=\"4475\" data-end=\"4482\">227<\/strong>).<\/p>\n<p data-start=\"4486\" data-end=\"4607\"><strong data-start=\"4486\" data-end=\"4505\">Interpretation:<\/strong> I see a one-point Knicks edge \u2014 a low-margin game where line movement and late scratches will matter.<\/p>\n<hr data-start=\"4609\" data-end=\"4612\" \/>\n<h1 data-start=\"4614\" data-end=\"4668\">5) News &amp; injury check (most important recent items)<\/h1>\n<ul data-start=\"4669\" data-end=\"5168\">\n<li data-start=\"4669\" data-end=\"4848\">\n<p data-start=\"4671\" data-end=\"4848\"><strong data-start=\"4671\" data-end=\"4705\">Mitchell Robinson \u2013 listed OUT<\/strong> for Knicks (big for defensive rim presence \/ rebounds). That\u2019s the largest Knicks-specific negative.<\/p>\n<\/li>\n<li data-start=\"4849\" data-end=\"4989\">\n<p data-start=\"4851\" data-end=\"4989\"><strong data-start=\"4851\" data-end=\"4892\">Josh Hart \u2013 limited \/ still adjusting<\/strong> after missing time (may reduce bench scoring\/defense).<\/p>\n<\/li>\n<li data-start=\"4990\" data-end=\"5168\">\n<p data-start=\"4992\" data-end=\"5168\">Bucks: Giannis scoring big recently (hot), Bucks generally healthier in rotation (no single massive absence reported in major previews).<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"5170\" data-end=\"5389\">(Checking public injury reports and previews \u2014 ESPN, Action Network, local beat \u2014 before locking a bet is essential because late scratches will swing a 1\u20132 point expected margin.)<\/p>\n<hr data-start=\"5391\" data-end=\"5394\" \/>\n<h1 data-start=\"5396\" data-end=\"5464\">6) Compare averaged model prediction vs my prediction &amp; final pick<\/h1>\n<h2>My PICK: New York Knicks Moneyline -120<\/h2>\n","protected":false},"excerpt":{"rendered":"<p>1) Which models I checked (top\/ reputable sources) BetQL (paywalled model; site shows they have a simulation-based projection). ESPN (BPI \/ Matchup Predictor \u2014 gives<\/p>\n","protected":false},"author":7,"featured_media":29819,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"rop_custom_images_group":[],"rop_custom_messages_group":[],"rop_publish_now":"initial","rop_publish_now_accounts":[],"rop_publish_now_history":[],"rop_publish_now_status":"pending","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[4],"tags":[2307,382,1227,2308,196,310,883,2306],"class_list":["post-29822","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nba","tag-ai-analysis-for-nba","tag-ai-nba-analysis","tag-ai-prediction-tool","tag-ai-predictions-nba","tag-free-nba-game-analysis","tag-nba-ai-game-prediction","tag-nba-ai-picks","tag-nba-player-props","two-columns"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/atswins.ai\/blog\/wp-content\/uploads\/2025\/10\/NBA-New-York-Knicks-vs.-Milwaukee-Bucks.png","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/posts\/29822","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/comments?post=29822"}],"version-history":[{"count":1,"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/posts\/29822\/revisions"}],"predecessor-version":[{"id":29823,"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/posts\/29822\/revisions\/29823"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/media\/29819"}],"wp:attachment":[{"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/media?parent=29822"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/categories?post=29822"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/atswins.ai\/blog\/wp-json\/wp\/v2\/tags?post=29822"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}