Undeniable does not add full accuracy to minions via accuracy mastery
Check version
- [X] I'm running the latest version of Path of Building and I've verified this by checking the changelog
Check for duplicates
- [X] I've checked for duplicate issues by using the search function of the issue tracker
Check for support
- [X] I've checked that the calculation is supposed to be supported. If it isn't please open a feature request instead (Red text is a feature request).
What is the value from the calculation in-game?
When using a Juggernaut with Undeniable while also taking the Accuracy Mastery of "Minion's accuracy rating is equal to yours" what should happen is minion's accuracy should be set to yours after all modifiers.
What is the value from the calculation in Path of Building?
What actually happens is regardless of strength, only your base accuracy and accuracy mods affect the minion accuracy but the "gain accuracy rating equal to twice your strength" is not added to minions, despite the wording of "Gain X Accuracy Rating" affecting you and minions correctly as custom modifier
Undeniable with 1000 strength and "Minion's accuracy rating is equal to yours" as custom mod:
Player accuracy (with random banishing blade, heavy strike)
Minion accuracy (SRS)
Same scenario but "Gain 1000 accuracy rating" as custom mod:
Player
Minion
How to reproduce the issue
- Select Undeniable ascendancy perk as a Juggernaut
- Select "Minion's accuracy rating is equal to yours" in Accuracy Mastery
- Add 1000 (or any amount really it doesn't matter) strength to the build
Character build code
eNq9XFt32zgOfm5-hY4f9mXT2JJ8zSYzx7lnNmkzdtrO7EsPI9E2p5Tokagknj373xegLpYvVCRbk_Y0lUUABD6CIEDSOfn51ePGMw1CJvzThnnUahjUd4TL_Olp48vj1cd-4-efDk4eiJx9npxFjGPLTwcfTtSzwekz5cDXAj5JgimVX1NZ9nd4Nye-nFHh35M_RHAt3NPGJ-HThvFEfJfJ9JPDSRh-Ih49bdyTgEQuDRoGCR3qu-fLpl-i6ZQGPokkcMyAzpE0uEMFhpEU98IFmgnhIQj0CPPHwvlB5XUgovlpw2oYz4y-xES39w-fR4855ZifVw6s-3DywMmCBmNJpBHCj9PGEEAiU3pBPPgJ0giPQJTdMY-65qDV6Zv2YGA3moXMZ1EQyt0kjOeUuhmTedTpWzrSh4BeTibUkeyZngdMns-I7yw7HBzZXR3rDuT3EZdszhkO2VI7HcfNhnx0HQ3xo5CEXzyMM9pOp3tkdU27PRj0enavmE_IjE_bwzcmZ2cckN2hF-S9nfpM0h2ZHwQLhb-HfXlWrYnnEecwZUvRjmhIg2ci2apaetnCe2L-TujdE5-ci3A5Rp0iygcaQDCQKwytNxjG1BEQP_IsvaOBWaKb7cza_u7YhJanrGRKwlBVm93suByXpasseDeFRhAny1GORcRLUsogH3oKpsKfK6QdrX9e0NeMzGoXyMsTmra251t_aYZZJG-FsEi_Z4Ez-m1sVFy5vHlYSu1Z_aNWr983YSprVXmYLULmEH5PXpkXeRDiH8kP6uekdPp6b53OpA_xScvcGmiXnysW0F34zgV3d-KbERHuwohzMg9Hu2CVd46R-tZ3yk31L36ggnY-OSjoAFlGMAUxH3nitDTPsptkKpdZv-POptRPelzkmDpFTHeUOrNryOFGRNJyAT-j6rbNQnSRuBS6SLgF3QL5qxwVgEJGDVDmkVXEVRGpS58G08V4xih3q1Gnmp2TeYkgi0DnuUsBvtrdNjRKsVaE5BsJ3HJLUVWdnkmYD7tmtxiumLyca1LIWYHBpWvJdEtfE4g_sCLg1diGgSei5Spo2YUmxNR5C8zOW4tGXA2NqBs55RapMw51XVn9QS3OK3EMpSTOjwvhTmmlTipxZOWZYh1H8zkEDRz9sgJw5YNkneUyno_dEtSfwXVLzWBcIst3sKQu3UG28JfvZY2lvC24bFcwZkleuotsQO8hOHgQ9VW5fi9yi7J2cKA-K1VsKcKSRd-DeAHNZ7ghE1ajhvRmuTWhVSWg_l-L0vJXyEt1cOm7UYBToXQf6xzbunlkHgTOMLwgkhhukhZ_JQEjvrRwiIyQksCZ3cHQXxHOnyASnDbyb_HTGqOZju1JU22J4dOtNxeBNOgr_vdAArlId6YUoXoDckLJfFVsQ9jhvGGMZ-Jl6D6jFY9C8DDbziLzOfXdFRmPAaUGSYOIg0ooG_GD4ZFQwhoVeyWI-a_dsSzzcNC1rf-tbKzduspsX4Ayp4122xr0Dts9SAYP7W6nbR-aXbvfOoy5LcvqHHZag1b7sDvom4e2ZZpAZ7aAojNoD9qH8M-0DqFW6HaAuteyD9u23WsjgFj0kWAxXO3aZ2C1BEtym4awxsT7gbFuaNSHky-jO_XwYSblPDxuNl9eXo7mRM7EhL7ConbkCK85ByaA42P4g3H-EcU2h_DnbHp5dkFav_wR9L9_v_W-nt_dXFtn8mry26cO-TzzXn4d_R50eTQcXnIB7arDZtrjSbyNGDbjTxgRAgZoxR7VRLjV2ON44MMnIWmIbfgy_XAyRpVCIwR3uKZeeLaAWXyFycraHkoyoEg9pjJ2yTzPaUMGEUUfnJCI4_tfI8IZ-lcr__Yu3pb1ReBldRmIAv_CxSaW-LiY46AP7-7iliGXiTDsLnW22KkShQyWGxP1Uu20DpdanxPuoCc1DOY7PHKhkEkCXDLanDyhZrjLjAWIm1q0Jifr5sMJaJMQX3PxRLiVsqhxvmc-OE6-b3OlJXk5Iiyk7n8Qa-BMdq0twGxKPXS0eyqJC2GheSvB-CYi0FRC4UnxpqzP8axHnpX3qsvtb3MqKu3-zA1Z8oyMF_HoNVaMNVNj1-ByROTH_uETL4kAqmdjS9dr5jd3B7YacOPI81AEBF0p_HAFvI22DMDtLe8FYty7sb37FMi09f2gVP2ihhDWNp0wa1j1wtXX7-uG2_pO0YXaGPJcSUJvHwA3J3kqHYo1yBLHcxYwuZvPrkrYcNvV5jXP3dL4zs4ba2BsUUGD0ZujUBLDbG2Je7meCcih_X8zTG42QMy1JowrUKo3W-T8zdjV4Zm7eNw5gbwCTBWcelvAWm1e87gzGNB8yzu7W6KbsalCvRN-F1ivOGirA3XZCLnPbxuwYtm83vrO0CoNY2ANMTFuRODSUANxztR3BvnW0UKcNgGEv28AvKXxnfEFDZbojmdEQtkEYVGDcGbnO-M7ghJEB3CubX0dWmt57wwK8iNaGBMURR7Qk9jut8sL8z3Ki2ojdUPJ82IsA_ZjNT9beZ-N0Mrbv2sgVCdG0ssGwskjVHWqfo0rVHxUGCuKW38eSSXwtIEVYjyADeNJwKARP-m7uUmLsr77kfeEl0Li_5c7YHlKj4XO96doMsGLOYCPDNS9o8urq8vzx9uvlwnLmKrNYSOMnsL48bTxldEXpfQFjArjIWLAOZmHNNsoUeVqYiUHvgJpiuqGZZdztstaEuglXb7SQIIV30jgBIxq9cra31Aq7hDPHHD7SicNb7voBcWb2ucklPGZiAYpdcVILwVv-2jNOc_yyK28t96ccG3PSesbSEjcsADPZhPm4M5f8ZDj9kZMVYCL40QBcRYF453syOtlqOtDOgFxo545vgKk405aC1BV14-0qMatevYL6hCt7XGjnjk7ZhP-DVYZ26VkVAWSPglfOTlMmiHjuHuuHdlLTjMSvcDPckaDZPNTJ-keIk9KUjhxAvYUSf00zlEUYKXO_zUIYZueNT7b1tiAbQWRaOWwVwNonkYvKj4k1QayItb4JEWLX3IuUzAEyRGkBv64tQCE9BRWY3_SXDBJVPwdPgvmxmdzmumyRlYUMCAx21-MOnDcX8z6CeT-EqEOCX9oxztp1bN_kQwzoC1S4kynlBCcVPtJwLm1n4TReiKx5B0VpxAqfkH6RwHowgCW0RSMr4z8C1jkZMHYlhSl1No-EZamVZIVh_KtllaWGDtocieryIdjkjcEwVp0U5DtlJOUnQpDJs7xvq_g-wncuHu2l51ChsR3L_BWy56G4qWYaA7CUs0-b0tXl0O6LvWkmRYd6lwTU_vk0BWqF6zm_hLC-_208bHd6x0Nelanb9mtdseOG5LDrm5ywAU53wUD5APlMWm3SPjbaaPf6h5Z1sA0LbxtH1-tOlHlXHL2hs_p0VsU0vi66jdK5lA642usMuMyBgjz52EjqPnk4tgYDUeXB5_AACQ4OCM-C2e4G3vGiUsPzgMykdQ9NlCrg4eATtjrsYFfOyj4MIYCqcSH5PTu2LBaB8mp5bFx_TH7e6CAGtE_j41e6wBybs4chjTmQVwgGJjE46WcpGgMDTb1RUANyBK8hXEvfDxWNiAJw2kLZPHijdEgVNUlVHEj4k-pEeBPKGiPOo0Eo-S8FEHJ4MMTRR3Gq0eNXEgDclnv4enL6A4dIi4fYx4DtwNkXKKbaaGrYzmjXBrDp0UYopkKJcNa8rfe4k-7XJfRqS7DqkGGaYxfyHxdULcGg7o1GFRFxpmAeLQuwK4g4IbCarIxtmYVFbb4xk4wbBuTTg1jYtYgo10RkLp8so5JVkWGys8qob7VA609_ceurHKVAbrm4pmG-7jJ9lnTrSyhspnm3mZWGduhF3Eqa3BBu4awaO9tersy3FX8eIT5irVr4Kts3L5TtFMZjP2XFWtvCfvr0N4XuHZdsd2upIm7MOJtm_rjVqeu1dqsxyJr3yGqnEHtHVq69Vje2VsRu2rEMvcF26whttc2pzr1jEN3z_xl_2xk3wyqpqlo1xUaahO0S2mxGxZ1-WRNQ7HzHNnTk2pbHawaAkUdMsy6DKotam0Kivd80msGaqtN3TIQ_oRNNy4EOFEohXcv3HB5G-Cf-D1t_ET9qZw1D_45MPu4Q6U-HsSXEsJ_gFLhvwySnOgauOMNawILDYp3KgwpjAW4Ytg8wNNIQ4lcI24uv1vi0JngLg0SrSjufyW_gSO9ztDLf51zG33-d2mkTJ1ilnS2Zlcmela3_3Yv6aWFlM3WaxayKeOfJ-obP2NJ1Fdr1i9obHJ52e_4wN9bQQO8u4MXCvDSyZjySU7GGxYmX_cTnKuNwjycxYzZOV7K0Lf7b0Cz-hXDnI7WoJ1uOydeeNJc_wU3_wefU_VZ
Screenshots
No response